Paper Digest: CIKM 2025 Papers & Highlights
The ACM Conference on Information and Knowledge Management (CIKM) is an annual computer science research conference dedicated to information management and knowledge management. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
Note: CIKM-2025 accepts more than 800 papers, this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All CIKM-2025 papers in a separate page.
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TABLE 1: Paper Digest: CIKM 2025 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | CCAgent: Coordinating Collaborative Data Scaling for Operating System Agents Via Web3 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the Operating System (OS) Agent field faces a significant data sparsity challenge due to the lack of public data collection systems and privacy concerns. To address this, we introduce CCAgent Net, a system that coordinates and incentivizes internet users to contribute to scaling OS agent datasets. |
Liang Chen; Haozhe Zhao; Yinzhen Huang; Yang Luo; Tsekai Lin; Weichu Xie; Ruoyu Wu; Peiyi Wang; Runxin Xu; Ming Wu; Baobao Chang; |
| 2 | Exploring The Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Text-based collaborative filtering (TCF) has emerged as the prominent technique for text and news recommendation, employing language models (LMs) as text encoders to represent items. |
Ruyu Li; Wenhao Deng; Yu Cheng; Zheng Yuan; Jiaqi Zhang; Fajie Yuan; |
| 3 | A Content-Driven Micro-Video Recommendation Dataset at Scale Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: However, the lack of publicly available large-scale micro-video datasets presents a challenge for developing effective recommender systems. To address this challenge, we introduce a comprehensive and diverse micro-video recommendation dataset, referred to as ”MicroLens.” |
Yongxin Ni; Yu Cheng; Xiangyan Liu; Junchen Fu; Youhua Li; Xiangnan He; Yongfeng Zhang; Fajie Yuan; |
| 4 | LLM4ES: Learning User Embeddings from Event Sequences Via Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents LLM4ES, a novel framework that exploits large pre-trained language models (LLMs) to derive user embeddings from event sequences. |
Aleksei Shestov; Omar Zoloev; Maksim Makarenko; Mikhail Orlov; Egor Fadeev; Ivan Kireev; Andrey Savchenko; |
| 5 | Constraint Back-translation Improves Complex Instruction Following of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this work, we find that existing datasets inherently contain implicit complex constraints and propose a novel data generation technique, constraint back-translation. |
Yunjia Qi; Hao Peng; Xiaozhi Wang; Bin Xu; Lei Hou; Juanzi Li; |
| 6 | Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: What makes this seemingly simple regression problem even more challenging is the lack of any principled way to account for the underlying uncertainties, due to missing or unrecorded experimental process (commonly happens in chemical labs). Given these challenges, we propose a new formulation for yield prediction. |
Kehan Guo; Zhen Liu; Zhichun Guo; Bozhao Nan; Olexandr Isayev; Nitesh Chawla; Olaf Wiest; Xiangliang Zhang; |
| 7 | Evolving Graph-Based Context Modeling for Multi-Turn Conversational Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, they do not leverage historically relevant passages effectively. To overcome these limitations, we propose EvoRAG, a novel framework that maintains an evolving knowledge graph aligned with the unstructured conversational context. |
Yiruo Cheng; Hongjin Qian; Fengran Mo; Yongkang Wu; Zhonghua Li; Qi Ye; Ji-Rong Wen; Zhicheng Dou; |
| 8 | LLMAEL: Large Language Models Are Good Context Augmenters for Entity Linking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: With the observation that LLMs are more adept at context generation instead of EL execution, we introduce LLM-Augmented Entity Linking (LLMAEL), the first framework to enhance specialized EL models with LLM data augmentation. |
Amy Xin; Yunjia Qi; Zijun Yao; Fangwei Zhu; Kaisheng Zeng; Bin Xu; Lei Hou; Juanzi Li; |
| 9 | KRAFT: A Knowledge Graph-Based Framework for Automated Map Conflation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: (2) They are heuristic algorithmic approaches that are based on pre-defined rules, unable to learn entities matching in a data-driven manner. To address these limitations, we design KRAFT, a learning based approach consisting of three parts: (1) Knowledge Graph Construction – where each GDB is represented by a knowledge graph, (2) Map Matching – where we use a knowledge graph alignment method as well as a geospatial feature encoder to match entities in obtained knowledge graphs, and (3) Map Merging – where we merge matched entities in the previous modules in a consistent manner, using a mixed integer linear programming formulation that fully merges the GDBs without adding any inconsistencies. |
Farnoosh Hashemi; Laks V.S. Lakshmanan; |
| 10 | Think It Image By Image: Multi-Image Moral Reasoning of Large Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While previous efforts have aimed to evaluate and improve the moral reasoning capabilities of VLMs, existing approaches are limited by simplified, unimodal settings or overly static visual scenarios. We propose a novel multi-image-based dataset pipeline MIST (Moral Inference through Storytelling with Text and Images) designed to assess moral reasoning in complex, dynamic scenarios to address these limitations. |
Chujie Gao; Yue Huang; Xiangqi Wang; Siyuan Wu; Nitesh V. Chawla; Xiangliang Zhang; |
| 11 | OBDD-NET: End-to-End Learning of Ordered Binary Decision Diagrams Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing search-based methods are still limited in scalability regarding dataset size, since they must explicitly encode the satisfaction of all examples in a dataset. To tackle this challenge, we introduce an OBDD encoding method to parameterize a neural network. |
Junming Qiu; Rongzhen Ye; Weilin Luo; Kunxun Qi; Hai Wan; Yue Yu; |
| 12 | ClariLM: Enhancing Open-domain Clarification Ability for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose ClariLM to synthesize large-scale clarification data and enhance the LLMs’ clarification capability. |
Ziliang Zhao; Haonan Chen; Shiren Song; Jian Xie; Zhicheng Dou; |
| 13 | FollowGPT: A Framework of Follow-up Question Generation for Large Language Models Via Conversation Log Mining Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose FollowGPT, a model that mines user follow-up intents from user-LLM conversational logs. |
Ziliang Zhao; Shiren Song; Zhicheng Dou; |
| 14 | Enhancing Fake News Video Detection Via LLM-Driven Creative Process Simulation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing datasets do not adequately reflect such relationships due to the difficulty of collecting and annotating large-scale real-world data, resulting in sparse coverage and non-comprehensive learning of the characteristics of potential fake news video creation. To address this issue, we propose a data augmentation framework AgentAug that generates diverse fake news videos by simulating typical creative processes. |
Yuyan Bu; Qiang Sheng; Juan Cao; Shaofei Wang; Peng Qi; Yuhui Shi; Beizhe Hu; |
| 15 | TimeRAG: Enhancing Complex Temporal Reasoning with Search Engine Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current Retrieval-Augmented Generation (RAG) methods primarily focus on retrieving the latest information but often fail to perform sophisticated temporal reasoning. To address this gap, we propose TimeRAG, a novel RAG framework designed to dynamically handle complex temporal reasoning tasks. |
Zhao Wang; Ziliang Zhao; Zhicheng Dou; |
| 16 | VocQuiz: Vocabulary Question Generation for English Language Education Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To better meet the demands of English teaching institutions, we present VocQuiz, a vocabulary question generation system that 1) combines generalization capabilities of LLMs with reliable language resources, including dictionaries, NLP datasets and authentic corpora, to enhance both contextual relevance and linguistic accuracy; 2) supports multiple question types, such as similar word selection and word collocation, to accommodate various instructional requirements; and 3) employs an iterative workflow to iteratively generate and refine questions, ensuring high-quality outputs and consistent assessment standards. |
Yongqi Li; Jiajun Wu; Shangqing Tu; Jifan Yu; Huiqin Liu; Lei Hou; Juanzi Li; |
| 17 | LangPTune: Optimizing Language-based User Profiles for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present LangPTune, the first end-to-end training framework designed to directly optimize LLM-generated user profiles for recommendation tasks. |
Zhaolin Gao; Joyce Zhou; Yijia Dai; Thorsten Joachims; |
| 18 | Dense Retrieval for Aggregated Search Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing dense retrieval models have limitations in: 1) capturing the structural information of search results ; and 2) generalizing across different vertical domains where the search results have different or even unseen structures. In this paper, we aim to tackle these limitations, and propose an effective and efficient dense retrieval model for aggregated search. |
Lang Mei; Sijie Liu; Ziyuan Zhao; Rolan Yan; Jiaxin Mao; Ji-rong Wen; |
| 19 | ClimateBench-M: A Multi-Modal Climate Data Benchmark with A Simple Generative Method Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Abstract: Climate science studies the structure and dynamics of Earth’s climate system and seeks to understand how climate changes over time, where the data is usually stored in the format … |
Dongqi Fu; Yada Zhu; Zhining Liu; Lecheng Zheng; Xiao Lin; Zihao Li; Liri Fang; Katherine Tieu; Onkar Bhardwaj; Kommy Weldemariam; Hanghang Tong; Hendrik Hamann; Jingrui He; |
| 20 | PyG-SSL: A Graph Self-Supervised Learning Toolkit Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Recognizing the growing interest within the research community, there is an urgent need for a comprehensive, beginner-friendly, and accessible toolkit consisting of the most representative graph SSL algorithms. To address these challenges, we present a Graph SSL toolkit named PyG-SSL, which is built upon PyTorch and is compatible with various deep learning and scientific computing backends. |
Lecheng Zheng; Baoyu Jing; Zihao Li; Zhichen Zeng; Tianxin Wei; Mengting Ai; Xinrui He; Lihui Liu; Dongqi Fu; Jiaxuan You; Hanghang Tong; Jingrui He; |
| 21 | EFT-LR: Benchmarking Learning Rate Policies in Parameter-Efficient Large Language Model Fine-tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, it lacks a systematic benchmark framework to explore and understand how different LR policies influence the effectiveness of parameter-efficient LLM fine-tuning, which makes it challenging to select an optimal LR policy. To address this critical research gap, this paper introduces a systematic benchmark, EFT-LR, for assessing and selecting LR policies for effective parameter-efficient fine-tuning of LLMs. |
Md Tasnim Jawad; Yanzhao Wu; |
| 22 | Jailbreaking LLMs Through Alignment Vulnerabilities in Out-of-Distribution Settings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ObscurePrompt, a simple yet effective black-box jailbreak method inspired by fragile LLM alignment on Out-of-Distribution (OOD) inputs. |
Yue Huang; Jingyu Tang; Dongping Chen; Bingda Tang; Yao Wan; Lichao Sun; Philip Yu; Xiangliang Zhang; |
| 23 | Adaptive Heterogeneous Graph Neural Networks: Bridging Heterophily and Heterogeneity Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Heterogeneous graphs (HGs) are common in real-world scenarios and often exhibit heterophily. However, most existing studies focus on either heterogeneity or heterophily in … |
Qin Chen; Guojie Song; |
| 24 | Autonomous Reasoning-Retrieval for Large Language Model Based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing LLM-based RSs fail to fully leverage the complementary strengths of LLMs (e.g., world knowledge and reasoning capabilities) and TRMs (e.g., recommendation-specific knowledge and computational efficiency), resulting in shallow exploration of the item space. To address this limitation, we propose DeepRec, a novel LLM-based RS approach that facilitates autonomous multi-turn interactions between LLMs and TRMs for deep item space exploration. |
Bowen Zheng; Xiaolei Wang; Enze Liu; Xi Wang; Hongyu Lu; Yu Chen; Wayne Xin Zhao; Ji-Rong Wen; |
| 25 | BordaRAG: Resolving Knowledge Conflict in Retrieval-Augmented Generation Via Borda Voting Process Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In voting theory, on the other hand, the preference-based voting methods represented by the Borda Voting (BV) consider the whole preference order of voters over all candidates, enabling the selection of candidates that better represent the collective viewpoint. Inspired by such an insight, we propose BordaRAG, a model designed to better select the most appropriate documents from conflicting documents. |
Yuxin Li; Chen Xu; Jun Xu; Ji-Rong Wen; |
| 26 | Improving Text Embedding Models with Positive-aware Hard-negative Mining Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper we introduce a family of positive-aware mining methods that use the positive relevance score as an anchor for false negative removal. |
Gabriel de Souza P. Moreira; Radek Osmulski; Mengyao Xu; Ronay Ak; Benedikt Schifferer; Even Oldridge; |
| 27 | Towards Adaptive Personalized Conversational Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Most existing studies implicitly incorporate users’ personal information and conversational context using large language models without distinguishing the specific requirements for each query turn. |
Fengran Mo; Yuchen Hui; Yuxing Tian; Zhaoxuan Tan; Chuan Meng; Zhan Su; Kaiyu Huang; Jian-Yun Nie; |
| 28 | SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Then we show that Mamba excels at capturing long-term structures, while Transformer is more effective at modeling short-term dynamics. Building on this insight, we propose State Space Transformer (SST), a multi-scale hybrid model with expert modules: a Mamba expert for long-range patterns and a Transformer expert for short-term variations. |
Xiongxiao Xu; Canyu Chen; Yueqing Liang; Baixiang Huang; Guangji Bai; Liang Zhao; Kai Shu; |
| 29 | Fine-Grained Emotion Recognition Via In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, semantically similar examples often introduce emotional discrepancies, hindering accurate representations and causing errors. To address this, we propose Emotion In-Context Learning (EICL), which introduces emotionally similar examples and uses a dynamic soft-label strategy to improve query representations in the emotion reasoning process. |
Zhaochun Ren; Zhou Yang; Chenglong Ye; Haizhou Sun; Chao Chen; Xiaofei Zhu; Xiangwen Liao; |
| 30 | Towards Few-shot Chemical Reaction Outcome Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this task becomes particularly challenging in low-data scenarios, where novel reaction types lack sufficient training examples. To address this challenge, we propose FewRxn, a novel model-agnostic few-shot reaction prediction framework that enables rapid adaptation to unseen reaction types using only a few training samples. |
Yili Shen; Yijun Tian; Cheng-Wei Ju; Olaf Wiest; Xiangliang Zhang; |
| 31 | SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose SELF, a SurrogatE-Light Feature selection method for deep recommender systems. |
Pengyue Jia; Zhaocheng Du; Yichao Wang; Xiangyu Zhao; Xiaopeng Li; Yuhao Wang; Qidong Liu; Huifeng Guo; Ruiming Tang; |
| 32 | QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As a result, the two fundamentally different tasks’ goals were relatively separate, and there was a lack of consistent objective on their representations; (2) Representation Unlearning: The generated multi-modal representations are always stored in cache store and serve as extra fixed input of recommendation model, thus could not be updated by recommendation model gradient, further unfriendly for downstream training.Inspired by the two difficulties challenges in downstream tasks usage, we introduce a quantitative multi-modal framework to customize the specialized and trainable multi-modal information for different downstream models. |
Xinchen Luo; Jiangxia Cao; Tianyu Sun; Jinkai Yu; Rui Huang; Wei Yuan; Hezheng Lin; Yichen Zheng; Shiyao Wang; Qigen Hu; Changqing Qiu; Jiaqi Zhang; Xu Zhang; Zhiheng Yan; Jingming Zhang; Simin Zhang; Mingxing Wen; Zhaojie Liu; Guorui Zhou; |
| 33 | Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces Force Matching (ForM), a novel framework for generative modeling that represents an initial exploration into leveraging special relativistic mechanics to enhance the stability of the sampling process. |
Yang Cao; Bo Chen; Xiaoyu Li; Yingyu Liang; Zhizhou Sha; Zhenmei Shi; Zhao Song; Mingda Wan; |
| 34 | AppAgent-Pro: A Proactive GUI Agent System for Multidomain Information Integration and User Assistance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing agents operate in a purely reactive manner, responding passively to user instructions, which significantly constrains their effectiveness and efficiency as general-purpose platforms for information acquisition. To overcome this limitation, this paper proposes AppAgent-Pro, a proactive GUI agent system that actively integrates multi-domain information based on user instructions. |
Yuyang Zhao; Wentao Shi; Fuli Feng; Xiangnan He; |
| 35 | Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose FourierKAN-GCF, a novel framework that adopts Fourier Kolmogorov-Arnold Networks as efficient transformation modules within graph propagation layers. |
Jinfeng Xu; Zheyu Chen; Jinze Li; Shuo Yang; Wei Wang; Xiping Hu; Edith Ngai; |
| 36 | Uncertainty Quantification for Multiple-Choice Questions Is Just One-Token Deep Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, we show that fine-tuning a model on just 1,000 examples to adjust the probability of the first generated token, under the common prompting setup where the model is instructed to output only a single answer choice, can systematically distort a broad range of UQ methods across models, prompts, and domains, all while leaving answer accuracy unchanged. |
Qingcheng Zeng; Mingyu Jin; Qinkai Yu; Zhenting Wang; Wenyue Hua; Guangyan Sun; Yanda Meng; Shiqing Ma; Qifan Wang; Felix Juefei-Xu; Fan Yang; Kaize Ding; Ruixiang Tang; Yongfeng Zhang; |
| 37 | Addressing Personalized Bias for Unbiased Learning to Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce personalized factors into the ULTR framework, which we term the user-aware ULTR problem. |
Zechun Niu; Lang Mei; Liu Yang; Ziyuan Zhao; Qiang Yan; Jiaxin Mao; Ji-Rong Wen; |
| 38 | Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Consequently, we introduce our benchmark, Scenario-Wise Rec, which comprises six public datasets and twelve baseline models, along with a training and evaluation pipeline. |
Xiaopeng Li; Jingtong Gao; Pengyue Jia; Xiangyu Zhao; Yichao Wang; Wanyu Wang; Yejing Wang; Yuhao Wang; Huifeng Guo; Ruiming Tang; |
| 39 | DinoCompanion: An Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce DinoCompanion, the first attachment-theory-grounded multimodal robot for emotionally responsive child-AI interaction. |
Boyang Wang; Yuhao Song; Jinyuan Cao; Peng Yu; Hongcheng Guo; Zhoujun Li; |
| 40 | Enhancing Dual-Target Cross-Domain Recommendation Via Similar User Bridging Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, in many real-world scenarios, overlapping data is extremely limited-or even entirely absent-significantly diminishing the effectiveness of these methods. To address this challenge, we propose SUBCDR, a novel framework that leverages large language models (LLMs) to bridge similar users across domains, thereby enhancing dual-target cross-domain recommendation. |
Qi Zhou; Xi Chen; Chuyu Fang; Jianji Wang; Chuan Qin; Fuzhen Zhuang; |
| 41 | CyberBOT: Ontology-Grounded Retrieval Augmented Generation for Reliable Cybersecurity Education Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In cybersecurity education, where accuracy and safety are paramount, systems must go beyond surface-level relevance to provide information that is both trustworthy and domain-appropriate. To address this challenge, we introduce CyberBOT. |
Chengshuai Zhao; Riccardo De Maria; Tharindu Kumarage; Kumar Satvik Chaudhary; Garima Agrawal; Yiwen Li; Jongchan Park; Ying-Chih Chen; Yuli Deng; Huan Liu; |
| 42 | Learning to Comparison-Shop Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel ranking architecture – Learning-to-Comparison-Shop (LTCS) System – that explicitly models and learns users’ comparison shopping behaviors. |
Jie Tang; Daochen Zha; Xin Liu; Huiji Gao; Liwei He; Stephanie Moyerman; Sanjeev Katariya; |
| 43 | Understanding The Embedding Models on Hyper-relational Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we data-wise convert HKGs to KG format using decomposition methods and then evaluate several classical KGE models’ performance on HKGs. |
Yubo Wang; Shimin Di; Zhili Wang; Haoyang Li; Fei Teng; Hao Xin; Lei Chen; |
| 44 | ReDSM5: A Reddit Dataset for DSM-5 Depression Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This limits both clinical relevance and interpretability. To address this gap, we introduce ReDSM5, a novel Reddit corpus comprising 1484 long-form posts, each exhaustively annotated at the sentence level by a licensed psychologist for the nine DSM-5 depression symptoms. |
Eliseo Bao; Anxo Perez; Javier Parapar; |
| 45 | Efficient Multimodal Streaming Recommendation Via Expandable Side Mixture-of-Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This presents two key challenges in streaming scenarios: the high cost of fine-tuning large multimodal encoders, and the risk of forgetting long-term user preferences due to continuous model updates. To tackle these challenges, we propose Expandable Side Mixture-of-Experts (XSMoE), a memory-efficient framework for multimodal streaming recommendation. |
Yunke Qu; Liang Qu; Tong Chen; Quoc Viet Hung Nguyen; Hongzhi Yin; |
| 46 | AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: It gives non-technical users a complete, visual solution to incrementally build and refine their knowledge bases, allowing multi-round dialogues and dynamic updates without specialized query languages. |
Xinjie Zhao; Moritz Blum; Fan Gao; Yingjian Chen; Boming Yang; Luis Marquez-Carpintero; M\'{o}nica Pina-Navarro; Yanran Fu; So Morikawa; Yusuke Iwasawa; Yutaka Matsuo; Chanjun Park; Irene Li; |
| 47 | Asymmetric Diffusion Recommendation Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a novel and effective method, named Asymmetric Diffusion Recommendation Model (AsymDiffRec), which learns forward and reverse processes in an asymmetric manner. |
Yongchun Zhu; Guanyu Jiang; Jingwu Chen; Feng Zhang; Xiao Yang; Zuotao Liu; |
| 48 | StoryWriter: A Multi-Agent Framework for Long Story Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present StoryWriter, a modular and open-source multi-agent framework for controllable and scalable long story generation. |
Haotian Xia; Hao Peng; Yunjia Qi; Bin Xu; Juanzi Li; Hou Lei; Xiaozhi Wang; |
| 49 | Controlled Feature Interaction Selection for Deep Sparse Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Deep sparse networks (DSNs) have demonstrated exceptional performance for nonlinear estimation and feature selection, which is crucial for enhancing predictive performance and … |
Yuhang Qiu; Biqin Song; Hong Chen; |
| 50 | MTGR: Industrial-Scale Generative Recommendation Framework in Meituan Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these generative approaches require abandoning the meticulously constructed cross features of traditional recommendation models,leading to a significant decline in model performance. To address this challenge, we propose Meituan Generative Recommendation, which is based on the HSTU architecture and is capable of retaining the original deep learning recommendation model (DLRM) features, including cross features. |
Ruidong Han; Bin Yin; Shangyu Chen; He Jiang; Fei Jiang; Xiang Li; Chi Ma; Mincong Huang; Xiaoguang Li; Chunzhen Jing; Yueming Han; MengLei Zhou; Lei Yu; Chuan Liu; Wei Lin; |
| 51 | Multi-Turn Interactions for Text-to-SQL with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, current interaction-based approaches either lack a step-by-step, interpretable SQL generation process or fail to provide a universally applicable interaction design. To address these challenges, we introduce Interactive-T2S, a framework that generates SQL queries through direct interactions with databases. |
Guanming Xiong; Junwei Bao; Hongfei Jiang; Yang Song; Wen Zhao; |
| 52 | An LLM-based Behavior Modeling Framework for Malicious User Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we introduce an LLM-based behavior modeling framework with an expert handbook to enhance LLMs’ behavior reasoning. |
Meng Jiang; Wenjie Wang; Chongming Gao; Shaofeng Hu; Kaishen Ou; Hui Lin; Fuli Feng; |
| 53 | Heterogeneous Influence Maximization in User Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Second, IM methods fail to account for the willingness to interact. To solve these issues, we propose two models named HeteroIR and HeteroIM. |
Hongru Hou; Jiachen Sun; Wenqing Lin; Wendong Bi; Xiangrong Wang; Deqing Yang; |
| 54 | Towards Understanding Bias in Synthetic Data for Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we thoroughly investigate the reliability of synthetic test collections constructed using LLMs, where LLMs are used to generate synthetic queries, labels, or both. |
Hossein A. Rahmani; Varsha Ramineni; Emine Yilmaz; Nick Craswell; Bhaskar Mitra; |
| 55 | Bidirectional Temporal-Aware Modeling with Multi-Scale Mixture-of-Experts for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose BIM3, a novel framework that integrates BIdirectional temporal-aware modeling with Multi-Scale Mixture-of-Experts for MTSF. |
Yifan Gao; Boming Zhao; Haocheng Peng; Hujun Bao; Jiashu Zhao; Zhaopeng Cui; |
| 56 | HealthGenie: A Knowledge-Driven LLM Framework for Tailored Dietary Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Seeking dietary guidance often requires navigating complex nutritional knowledge while considering individual health needs. To address this, we present HealthGenie, an interactive platform that leverages the interpretability of knowledge graphs (KGs) and the conversational power of large language models (LLMs) to deliver tailored dietary recommendations alongside integrated nutritional visualizations for fast, intuitive insights. |
Fan Gao; Xinjie Zhao; Ding Xia; Zhongyi Zhou; Rui Yang; Jinghui Lu; Hang Jiang; Chanjun Park; Irene Li; |
| 57 | EFU: Enforcing Federated Unlearning Via Functional Encryption Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing FU methods maintain data privacy by performing unlearning locally on the client-side and sending targeted updates to the server without exposing forgotten data; yet they often rely on server-side cooperation, revealing the client’s intent and identity without enforcement guarantees – compromising autonomy and unlearning privacy. In this work, we propose EFU (Enforced Federated Unlearning), a cryptographically enforced FU framework that enables clients to initiate unlearning while concealing its occurrence from the server. |
Samaneh Mohammadi; Vasileios Tsouvalas; Iraklis Symeonidis; Ali Balador; Tanir Ozcelebi; Francesco Flammini; Nirvana Meratnia; |
| 58 | A Comparative Analysis of Linguistic and Retrieval Diversity in LLM-Generated Search Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents an empirical comparison of LLM- and human-generated queries across multiple dimensions, including lexical diversity, linguistic variation, and retrieval effectiveness. |
Oleg Zendel; Sara Fahad Dawood Al Lawati; Lida Rashidi; Falk Scholer; Mark Sanderson; |
| 59 | KUG: Joint Enhancement of Internal and External Knowledge for Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Nevertheless, existing approaches overlook the inherent distinctions between domain-specific knowledge and external factual sources during integration. To bridge this gap, we propose KUG (Knowledge-Update-Generation), a novel RAG framework that leverages internal knowledge semantics to ensure query enhancement efficacy, validates and dynamically updates knowledge representations using external evidence, and achieves systematic integration through knowledge graph embeddings. |
Mingyang Li; Shisong Chen; Shengkun Tu; Ziyi Du; Jinghao Zhang; Zhixu Li; Yanghua Xiao; |
| 60 | Can Large Vision-Language Models Understand Multimodal Sarcasm? Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Through comprehensive experiments, we identify key limitations, such as insufficient visual understanding and a lack of conceptual knowledge. To address these issues, we propose a training-free framework that integrates in-depth object extraction and external conceptual knowledge to improve the model’s ability to interpret and explain sarcasm in multimodal contexts. |
Xinyu Wang; Yue Zhang; Liqiang Jing; |
| 61 | ECLIPSE: Efficient Cross-Lingual Log Intelligence Parser with Semantic Entropy-Enhanced LCS Algorithm Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose ECLIPSE, an Efficient Cross-platform and Cross-lingual Log Intelligent Parsing framework with Semantic Entropy-Enhanced Longest Common Subsequence algorithm in industrial Environments. |
Wei Zhang; Xianfu Cheng; Xiang Li; Jian Yang; Liying Zhang; Xiangyuan Guan; Zhoujun Li; |
| 62 | HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, a key challenge lies in the noisy and sparse nature of spatial-temporal data, which limits existing neural networks’ ability to learn meaningful region representations in the spatial-temporal graph. To overcome these limitations, we propose HGAurban, a novel heterogeneous spatial-temporal graph masked autoencoder that leverages generative self-supervised learning for robust urban data representation. |
Qianru Zhang; Xinyi Gao; Haixin Wang; Dong Huang; Siu-Ming Yiu; Hongzhi Yin; |
| 63 | From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In contrast, deep learning models can effectively capture complex dynamics, yet often offer limited transparency. To bridge this gap, we propose a two-stage framework that integrates unsupervised pattern extracion with interpretable forecasting. |
Juwon Kim; Hyunwook Lee; Hyotaek Jeon; Seungmin Jin; Sungahn Ko; |
| 64 | MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods, typically based on stochastic denoising diffusion probabilistic models (DDPMs), suffer from high inference latency and variable outputs, limiting their applicability in real-world tabular settings. To address these deficiencies, we present in this paper MissDDIM, a conditional diffusion framework that adapts Denoising Diffusion Implicit Models (DDIM) for tabular imputation. |
Youran Zhou; Mohamed Reda Bouadjenek; Sunil Aryal; |
| 65 | ConsensNet: A Unified Consensus-Centric Framework for Incomplete Multi-View Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These approaches are generally suboptimal when facing high missing-view ratios and struggle to capture latent cross-view dependencies. To overcome these limitations, we propose ConsensNet, a unified consensus-centric framework for IMVC. |
Yifei Chen; Xiaolin Xiao; Yue-Jiao Gong; |
| 66 | Continuous Data Augmentation Via Condition-Tokenized Diffusion Transformer for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The two-stage methods, which train the SR model and the DM separately, overlook their potential complementarity. To address these challenges, we propose Continuous Data Augmentation via Condition-Tokenized Diffusion Transformer for Sequential Recommendation (CATDiT). |
Chenglong Shi; Haosen Wang; Pan Tang; |
| 67 | Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce TRIGON, a novel framework that constructs enriched, non-planar triangulations by learning to select relevant triangles from multiple graph views. |
Hugo Attali; Thomas Papastergiou; Nathalie Pernelle; Fragkiskos D. Malliaros; |
| 68 | ST-LINK: Spatially-Aware Large Language Models for Spatio-Temporal Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, the inherent limitations of LLMs in modeling spatial relationships and their architectural incompatibility with graph-structured spatial data remain largely unaddressed. To overcome these limitations, we introduce ST-LINK, a novel framework that enhances the capability of Large Language Models to capture spatio-temporal dependencies. |
Hyotaek Jeon; Hyunwook Lee; Juwon Kim; Sungahn Ko; |
| 69 | GraphIAM: Two-Stage Algorithm for Improving Class-Imbalanced Node Classification on Attribute-Missing Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose GraphIAM, a novel two-stage algorithm for improving class-imbalanced node classification on attribute-missing graphs. |
Riting Xia; Chunxu Zhang; Xueyan Liu; Anchen Li; Yan Zhang; |
| 70 | Budget and Frequency Controlled Cost-Aware Model Extraction Attack on Sequential Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel approach, named Budget and Frequency Controlled Cost-Aware Model Extraction Attack (BECOME), for extracting black-box sequential recommenders, which extends the standard extraction framework with two cost-aware innovations: Feedback-Driven Dynamic Budgeting periodically evaluates the victim model to refine query allocation and steer sequence generation adaptively. |
Lei Zhou; Min Gao; Zongwei Wang; Yibing Bai; |
| 71 | Harnessing Large Language Models for Group POI Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Group POI recommendation systems aim to satisfy the collective preferences of multiple users, but existing approaches face two major challenges: diverse group preferences and extreme data sparsity in group check-in data. To overcome these challenges, we propose LLMGPR, a novel framework that leverages large language models (LLMs) for group POI recommendations. |
Jing Long; Liang Qu; Junliang Yu; Tong Chen; Quoc Viet Hung Nguyen; Hongzhi Yin; |
| 72 | Multi-Task Learning Through Hierarchical Information Sharing and Transfer Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a novel Hierarchical Information Sharing and Transfer (HIST) framework for multi-task learning, which employs implicit shared-bottom pattern and explicit sequential transfer at tower-level simultaneously. |
Yufan Mao; Jingran Xu; Liang Zhang; Xiyue Hou; Yongbo Jin; Yingming Li; Linjian Mo; |
| 73 | Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models Via Conditional Front-Door Adjustment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel causal prompting framework, Conditional Front-Door Prompting (CFD-Prompting), which enables the unbiased estimation of the causal effect between the query and the answer, conditional on external knowledge, while mitigating internal bias. |
Bo Zhao; Yinghao Zhang; Ziqi Xu; Yongli Ren; Xiuzhen Zhang; Renqiang Luo; Zaiwen Feng; Feng Xia; |
| 74 | FedSTEP: Asynchronous and Staleness-Aware Personalization for Efficient Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although effective, deploying this architecture in real-world systems remains challenging due to the presence of stragglers and the high communication cost. To address these issues, we propose FedSTEP, a unified framework that integrates asynchronous training with dynamic communication sparsification. |
Gang Yan; Jian Li; Wan Du; |
| 75 | TLCCSP: A Scalable Framework for Enhancing Time Series Forecasting with Time-Lagged Cross-Correlations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While recent deep learning models have improved predictive capabilities, they often overlook time-lagged cross-correlations between related sequences, which are crucial for capturing complex temporal relationships. To address this, we propose the Time-Lagged Cross-Correlations-based Sequence Prediction framework (TLCCSP), which enhances forecasting accuracy by effectively integrating time-lagged cross-correlated sequences. |
Jianfei Wu; Wenmian Yang; Bingning Liu; Weijia Jia; |
| 76 | Correlation-aware Online Change Point Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This task becomes particularly challenging in the online setting, where different types of change can occur, including shifts in both the marginal and joint distributions of the data. In this paper, we address these challenges by tracking the Riemannian geometry of correlation matrices, allowing Riemannian metrics to compute the geodesic distance as an accurate measure of correlation dynamics. |
Chengyuan Deng; Zhengzhang Chen; Xujiang Zhao; Haoyu Wang; Junxiang Wang; Jie Gao; Haifeng Chen; |
| 77 | ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, conventional retrieval strategies, which are often based on holistic code-text embeddings, fail to capture the structural intricacies of code, resulting in suboptimal retrieval quality. To address the above limitations, we propose ReCode, a fine-grained retrieval-augmented in-context learning framework designed for accurate and efficient code repair. |
Yicong Zhao; Shisong Chen; Jiacheng Zhang; Zhixu Li; |
| 78 | LeadFairRec: LLM-enhanced Discriminative Counterfactual Debiasing for Two-sided Fairness in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose an LLM-Enhanced DiscriminAtive Counterfactual Debiasing Model for Two-sided Fairness in Recommendation (LeadFairRec). |
Yimin Hou; Yue Kou; Derong Shen; Xiangmin Zhou; Dong Li; Tiezheng Nie; Ge Yu; |
| 79 | A Comprehensive Toolkit for Generalized Robust Vision Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Adversarial attacks and data distribution shifts remain critical vulnerabilities, degrading model performance under practical conditions. To address these challenges and advance robustness research, we introduce a comprehensive, user-friendly toolkit for training, evaluating, and analyzing robust vision models. |
Zhao Li; Yuefeng Chen; Hui Xue; Xiaofeng Mao; |
| 80 | Dialogues Aspect-based Sentiment Quadruple Extraction Via Structural Entropy Minimization Partitioning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Instead, we propose utilizing a structural entropy minimization algorithm to partition the dialogues. |
Kun Peng; Cong Cao; Hao Peng; Zhifeng Hao; Lei Jiang; Kongjing Gu; Yanbing Liu; Philip S. Yu; |
| 81 | A Cost-Aware Approach for Collaborating Large Language Models and Small Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, directly compressing the prompt to reduce tokens often leads to a significant accuracy loss. To address the above challenges, we propose a cost-aware approach for collaborating LLMs and SLMs, named Coco. |
Zheng Li; Xuyun Zhang; Sheng Lu; Hua Deng; Hao Tian; Wanchun Dou; |
| 82 | Efficient Knowledge Graph Unlearning with Zeroth-order Information Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Since full retraining is costly, various machine unlearning methods have been proposed. |
Yang Xiao; Ruimeng Ye; Bohan Liu; Xiaolong Ma; Bo Hui; |
| 83 | Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: https://github.com/USTCAGI/PruningRAG, with the aim of advancing future research in the RAG community. |
Shuo Yu; Mingyue Cheng; Qi Liu; Daoyu Wang; Jiqian Yang; Jie Ouyang; Yucong Luo; Chenyi Lei; Enhong Chen; |
| 84 | IARD: Intruder Activity Recognition Dataset for Threat Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our contribution highlights the potential of IARD in advancing AI-driven surveillance systems, providing a foundational dataset and benchmark for recognizing complex intruder activities. |
Shehzad Ali; Md Tanvir Islam; Ik Hyun Lee; Saeed Anwar; Javier Del Ser; Khan Muhammad; |
| 85 | Multimodal Sentiment Analysis Via Progressive Fusion of Audio-Visual Affective Descriptions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In particular, conventional fusion methods have not effectively addressed the difficulty of integrating heterogeneous modality information. To tackle these challenges, we propose a progressive fusion framework based on audio-visual affective descriptions for MSA. |
Lisong Ou; Zhixin Li; |
| 86 | DeepAries: Adaptive Rebalancing Interval Selection for Enhanced Portfolio Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose DeepAries, a novel deep reinforcement learning framework for dynamic portfolio management that jointly optimizes the timing and allocation of rebalancing decisions. |
Jinkyu Kim; Hyungjung Yi; Mogan Gim; Donghee Choi; Jaewoo Kang; |
| 87 | High-Context Empathy in Conversations for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: After that, we propose an innovative High-context Empathy Network (HEN) to improve LLMs’ capabilities in generating high-context empathetic responses. |
Yuyan Chen; Lei Xia; Jinghan Cao; Zhendong Hou; Weinan Dai; Zhixu Li; |
| 88 | Towards Reliable GNNs: Adversarial Calibration Learning for Confidence Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In some cases, these methods even degrade calibration performance compared to the original uncalibrated models. To address this limitation, we introduce AdvCali, a novel framework that adaptively improves calibration across diverse node groups. |
Yilong Wang; Jiahao Zhang; Tianxiang Zhao; Suhang Wang; |
| 89 | Fine-Grained Graph Rationalization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose FIne-grained Graph rationalization (FIG). |
Zhe Xu; Menghai Pan; Yuzhong Chen; Huiyuan Chen; Yuchen Yan; Mahashweta Das; Hanghang Tong; |
| 90 | Where Do LLMs Go Wrong? Diagnosing Automated Peer Review Via Aspect-Guided Multi-Level Perturbation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose an aspect-guided, multi-level perturbation framework to systematically diagnose LLM weaknesses in automated peer review. |
Jiatao Li; Yanheng Li; Xinyu Hu; Mingqi Gao; Xiaojun Wan; |
| 91 | Differentiable Probabilistic Logic Reasoning For Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Neuro-symbolic approaches combine both by employing embeddings to approximate probabilistic distributions, yet face challenges in weight optimization and hurdles in scaling up from large probability graphs. To address these issues, we propose DPLogic, a differentiable probabilistic logic reasoning framework for KG completion. |
Zhongbin Li; Lixing Yu; Kun Yue; Xinquan Wu; |
| 92 | ESED: Emotion-Specific Evidence Decomposition for Uncertainty-Aware Multimodal Emotion Recognition in Conversation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While some recent methods incorporate uncertainty modeling, they often focus on overall prediction confidence, without explicitly distinguishing the different sources of uncertainty introduced by underlying factors. To address these challenges, we propose a novel Emotion-Specific Evidence Decomposition framework (ESED) that leverages evidential deep learning to explicitly model and disentangle multimodal emotional uncertainty. |
Zechang Xiong; Zhenyan Ji; Wenkang Kong; Jiuqian Dai; Shen Yin; |
| 93 | FreeGAD: A Training-Free Yet Effective Approach for Graph Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Surprisingly, our empirical findings suggest that the training phase of deep GAD methods, commonly perceived as crucial, may actually contribute less to anomaly detection performance than expected. Inspired by this, we propose FreeGAD, a novel training-free yet effective GAD method. |
Yunfeng Zhao; Yixin Liu; Shiyuan Li; Qingfeng Chen; Yu Zheng; Shirui Pan; |
| 94 | Revisiting Trajectories to Road: A New Diffusion Model and A New Dataset with 1,000,000,000 Points Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Additionally, current datasets are often outdated and come from a single mobility source, leading to biased urban dynamics and poor generalizability. To address these issues, we introduce DiffusionT2R, the first diffusion-based framework for T2R. |
Yang Wang; Miaomiao Li; Jiazhi Ni; |
| 95 | Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a novel Cross-experts Covariance Loss for Disentangled Learning model (Crocodile), which employs multiple embedding tables to make the model domain-aware at the embeddings which consist most parameters in the model, and a covariance loss upon these embeddings to disentangle them, enabling the model to capture diverse user interests among domains. |
Zhutian Lin; Junwei Pan; Haibin Yu; Xi Xiao; Ximei Wang; Zhixiang Feng; Shifeng Wen; Shudong Huang; Dapeng Liu; Lei Xiao; |
| 96 | Retrieval-Augmented Image Captioning Via Synthesized Entity-Aware Knowledge Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these methods make the model either imitate the caption style or fail to capture the relationship between entities, resulting in a lack of diversity or inaccuracy in the generated captions. To address these issues, we propose SEAR, a novel framework that utilizes external Synthesized Entity-Aware knowledge Representations to improve captioning performance. |
Lin Shen; Chenxu Cui; Jinchao Zhang; Haihui Fan; Haotian Jin; Bo Li; |
| 97 | Evaluating Robustness of LLMs in Question Answering on Multilingual Noisy OCR Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we conduct a comprehensive analysis of how OCR-induced noise affects the performance of Multilingual QA Systems. |
Bhawna Piryani; Jamshid Mozafari; Abdelrahman Abdallah; Antoine Doucet; Adam Jatowt; |
| 98 | Reverse Chain-of-Thought and Causal Path Verification: A Modular Plugin for Aligning LLMs with Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods often rely on prompt engineering or fixed templates, which obscure the relational structure and limit generalization. To address these limitations, this paper introduces the Reverse Chain-of-Thought (R-CoT) and Causal Path Verification Plugin, a modular framework that reconstructs retrieved KG triples into reverse chains of sub-questions. |
Dezhuang Miao; Yibin Du; Xiang Li; Xiaoming Zhang; Jiahe Li; Bo Zhang; Bingyu Yan; Lian Zhang; Litian Zhang; |
| 99 | OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation Using Sparse Drifter Trajectories Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate physical covariates without specialized sensors. In this paper, we introduce the OceAn Salinity Imputation System, a novel diffusion adversarial framework designed to address these challenges by: (1) employing a transformer-based global dependency capturing module to learn long-range spatio-temporal correlations from sparse trajectories; (2) constructing a generative imputation model that conditions on easily observed tidal covariates to progressively refine imputed salinity fields; and (3) using a scheduler diffusion method to enhance the model’s robustness. |
Bo Li; Yingqi Feng; Ming Jin; Xin Zheng; Yufei Tang; Laurent Cherubin; Can Wang; Alan Wee-Chung Liew; Qinghua Lu; Jingwei Yao; Hong Zhang; Shirui Pan; Xingquan Zhu; |
| 100 | Improving The Safety of Medication Recommendation Via Graph Augmented Patient Similarity Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such issues raise concerns about the safety of medication recommendation. To address this, we propose GPSRec, a novel Graph augmented Patient Similarity network for medication Recommendation. |
Ming He; Yongjie Zheng; Changle Li; Man Zhou; |
| 101 | Pantheon: Personalized Multi-objective Ensemble Sort Via Iterative Pareto Policy Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we provide Pantheon, a practical neural-network based ensemble sort. |
Jiangxia Cao; Pengbo Xu; Yin Cheng; Kaiwei Guo; Jian Tang; Shijun Wang; Dewei Leng; Shuang Yang; Zhaojie Liu; Yanan Niu; Guorui Zhou; Kun Gai; |
| 102 | EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel self-supervised framework, EmoPerso, which improves personality detection through emotion-aware modelling. |
Lingzhi Shen; Xiaohao Cai; Yunfei Long; Imran Razzak; Guanming Chen; Shoaib Jameel; |
| 103 | Thematic Bottleneck Models for Multimodal Analysis of School Attendance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To effectively analyse the data, we propose Thematic Bottleneck Models (TBMs) to enhance the understanding of subjective experiences behind data and the interpretability of attendance modelling. |
Tingrui Qiao; Caroline Walker; Chris Cunningham; Adam Jang-Jones; Susan Morton; Kane Meissel; Yun Sing Koh; |
| 104 | LiveVal: Real-time and Trajectory-based Data Valuation Via Adaptive Reference Points Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose LiveVal, a real-time and trajectory-based data valuation method that assesses training data by analyzing their influence on the optimization trajectory. |
Jie Xu; Zihan Wu; Cong Wang; Xiaohua Jia; |
| 105 | GCondenser: Benchmarking Graph Condensation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we introduce GCondenser, a large-scale graph condensation toolkit designed to facilitate flexible development, holistic evaluation and comparison of mainstream GC approaches. |
Yilun Liu; Ruihong Qiu; Zi Huang; |
| 106 | TalkDep: Clinically Grounded LLM Personas for Conversation-Centric Depression Screening Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we embrace the recent advanced language models as the backbone and propose a novel clinician-in-the-loop patient simulation pipeline, TalkDep, with access to diversified patient profiles to develop simulated patients. |
Xi Wang; Anxo Perez; Javier Parapar; Fabio Crestani; |
| 107 | Reconsidering The Performance of GAE in Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Recent advancements in graph neural networks (GNNs) for link prediction have introduced sophisticated training techniques and model architectures. |
Weishuo Ma; Yanbo Wang; Xiyuan Wang; Muhan Zhang; |
| 108 | Pruning Strategies for Backdoor Defense in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such attacks consist of stealthy malicious triggers introduced through subtle syntactic or stylistic manipulations, which can bypass traditional detection and remain in the model, making post-hoc purification essential. In this study, we explore whether attention-head pruning can mitigate these threats without any knowledge of the trigger or access to a clean reference model. |
Santosh Chapagain; Shah Muhammad Hamdi; Soukaina Filali Boubrahimi; |
| 109 | RerankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Evaluating the quality of retrieval-augmented generation (RAG) and document reranking systems remains challenging due to the lack of scalable, user-centric, and multi-perspective evaluation tools. We introduce RankArena, a unified platform for comparing and analysing the performance of retrieval pipelines, rerankers, and RAG systems using structured human and LLM-based feedback as well as for collecting such feedback. |
Abdelrahman Abdallah; Mahmoud Abdalla; Bhawna Piryani; Jamshid Mozafari; Mohammed Ali; Adam Jatowt; |
| 110 | NR-GCF: Graph Collaborative Filtering with Improved Noise Resistance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Nevertheless, a common oversight in existing studies is the presumption of the inherent reliability of these interactions, ignoring the reality that a significant fraction of user-item engagements, such as accidental clicks, are inherently noisy. Extensive studies have revealed that GNN is vulnerable to such noisy edges within the graph-structured data, as those noisy edges can mislead the network into overfitting incorrect patterns of interactions, thereby propagating such incorrect information through the entire interaction network.To address these challenges, in this paper, we propose a novel noise-robust GNN-based training strategy for recommendation, known as Noise-Resistant Graph Collaborative Filtering (NR-GCF). |
Yijun Chen; Bohan Li; Yicong Li; Lixiang Song; Haofen Wang; Wenlong Wu; Junnan Zhuo; Hongzhi Yin; |
| 111 | Few-Shot Knowledge Graph Completion Via Transfer Knowledge from Similar Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce TransNet, a transfer learning method for few-shot KG completion that captures task relationships and reuses knowledge from related tasks. |
Lihui Liu; Zihao Wang; Dawei Zhou; Ruijie Wang; Yuchen Yan; Sihong He; Hanghang Tong; |
| 112 | BOVIS: Bias-Mitigated Object-Enhanced Visual Emotion Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While recent advances in deep learning have significantly improved emotion detection capabilities, existing methods often fall short because of their exclusive focus on either holistic visual features or semantic content, thereby neglecting their interplay. To address this limitation, we introduce BOVIS, a Bias-Mitigated Object-Enhanced Visual Emotion Analysis framework. |
Yubeen Lee; Sangeun Lee; Junyeop Cha; Jufeng Yang; Eunil Park; |
| 113 | Forecasting The Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Classical regressors digest surface features but ignore context, while large language models (LLMs) excel at contextual reasoning but misestimate numbers. We present BuzzProphet, a reasoning-augmented hashtag popularity prediction framework that (1) instructs an LLM to articulate a hashtag’s topical virality, audience reach, and timing advantage; (2) utilizes these popularity-oriented rationales to enrich the input features; and (3) regresses on these inputs. |
Yifei Xu; Jiaying Wu; Herun Wan; Yang Li; Zhen Hou; Min-Yen Kan; |
| 114 | Towards Robust Continual Test-Time Adaptation Via Neighbor Filtration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To further ensure reliability, we introduce an OOD Neighbor Filtration technique that selects a subset of high-confidence samples based on entropy and neighbor similarity, ensuring consistency within the semantic neighborhood. |
Taki Hasan Rafi; Amit Agarwal; Hitesh L. Patel; Dong-Kyu Chae; |
| 115 | Sparse Autoencoders in Collaborative Filtering Enhanced LLM-based Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose using sparse autoencoders to improve input prompts. |
Xinyu He; Jose Sepulveda; Fei Wang; Hanghang Tong; |
| 116 | STARec: An Efficient Agent Framework for Recommender Systems Via Autonomous Deliberate Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities. |
Chenghao Wu; Ruiyang Ren; Junjie Zhang; Ruirui Wang; Zhongrui Ma; Qi Ye; Wayne Xin Zhao; |
| 117 | Relation-Faceted Graph Pooling with LLM Guidance for Dynamic Span-Aware Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present RePooL, a hierarchical validation framework that performs fine-grained token-level filtering followed by coarse-grained span-level validation, enabling robust multi-granular semantic modeling. |
Hye-Yoon Baek; Jinho Choi; Jimyeung Seo; Xiongnan Jin; Dongcheon Lee; Byungkook Oh; |
| 118 | SyLeR: A Framework for Explicit Syllogistic Legal Reasoning in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although existing large language models (LLMs) can generate responses to legal questions, they fail to perform explicit syllogistic reasoning, often producing implicit and unstructured answers that lack explainability and trustworthiness. To address this limitation, we propose SyLeR, a novel framework that empowers LLMs to engage in explicit syllogistic legal reasoning. |
Kepu Zhang; Weijie Yu; Zhongxiang Sun; Jun Xu; |
| 119 | PrLM: Learning Explicit Reasoning for Personalized RAG Via Contrastive Reward Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, such models are often sensitive to retrieval quality and may generate responses that are misaligned with user preferences. To address this limitation, we propose PrLM, a reinforcement learning framework that trains LLMs to explicitly reason over retrieved user profiles. |
Kepu Zhang; Teng Shi; Weijie Yu; Jun Xu; |
| 120 | OFIA: An Object-centric Fine-grained Alignment Enhancement for Video-Text Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the defects of the existing fine-grained alignment approach, this paper proposes the Object-centric Fine-grained Alignment Enhancement for Video-Text Retrieval, namely OFIA, which consists of a text-guided object-text alignment module and a similarity-wise frame aggregation module to enhance video-text alignment. |
Zhengqi Huang; Wei Li; Chuang Dong; Mingxin Liu; |
| 121 | T-Retrievability: A Topic-Focused Approach to Measure Fair Document Exposure in Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We hypothesise that an uneven distribution of retrievability scores across the entire collection may not accurately reflect exposure bias but rather indicate variations in topical relevance. As a solution, we propose a topic-focused localised retrievability measure, which we call T-Retrievability (topic-retrievability), which first computes retrievability scores over multiple groups of topically-related documents, and then aggregates these localised values to obtain the collection-level statistics. |
Xuejun Chang; Zaiqiao Meng; Debasis Ganguly; |
| 122 | Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a new framework based on Neural Stochastic Delay Differential Equations (Neural SDDEs) that combines stochastic modeling with neural networks to capture delayed temporal dynamics and handle irregular observations. |
Yongkyung Oh; Seungsu Kam; Dongyoung Lim; Sungil Kim; |
| 123 | GraphRCG: Self-Conditioned Graph Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As such, the overview of the entire distribution is not explicitly captured and used for graph generation. In contrast, in this work, we propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions and employ these distributions to guide the generation process. |
Song Wang; Zhen Tan; Xinyu Zhao; Tianlong Chen; Huan Liu; Jundong Li; |
| 124 | Hearing The Meaning, Not The Mess: Beyond Literal Transcription for Spoken Language Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these, we introduce CogTrans, a cognitively inspired speech-to-meaning framework. |
Min Sun; Ke Xu; Jiarong Liu; Jifan Yang; Yan Fang; Weizheng Wang; Qipeng Xie; Shuxin Zhong; Kaishun Wu; |
| 125 | Sequential Difference Maximization: Generating Adversarial Examples Via Multi-Stage Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we reconstruct the optimization objective for generating adversarial examples as ”maximizing the difference between the non-true labels’ probability upper bound and the true label’s probability,” and propose a gradient-based attack method termed Sequential Difference Maximization (SDM). |
Xinlei Liu; Tao Hu; Peng Yi; Weitao Han; Jichao Xie; Baolin Li; |
| 126 | Causality-aware Graph Aggregation Weight Estimator for Popularity Debiasing in Top-K Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel approach to mitigate popularity bias through rational modeling of the graph aggregation process. |
Yue Que; Yingyi Zhang; Xiangyu Zhao; Chen Ma; |
| 127 | Pet-Bench: Benchmarking The Abilities of Large Language Models As E-Pets in Social Network Services Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce PET-BENCH, a dedicated benchmark that evaluates LLMs across both self-interaction and human-interaction dimensions. |
Hongcheng Guo; Zheyong Xie; Shaosheng Cao; Boyang Wang; Weiting Liu; Zheyu Ye; Zhoujun Li; Zuozhu Liu; Wei Lu; |
| 128 | MISS: Multi-Modal Tree Indexing and Searching with Lifelong Sequential Behavior for Retrieval Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Multi-modal Indexing and Searching with lifelong Sequence (MISS), which contains a multi-modal index tree and a multi-modal lifelong sequence modeling module. |
Chengcheng Guo; Junda She; Kuo Cai; Shiyao Wang; Qigen Hu; Qiang Luo; Guorui Zhou; Kun Gai; |
| 129 | CLUE: Using Large Language Models for Judging Document Usefulness in Web Search Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CLUE, a user-centric evaluation method that explicitly incorporates users’ search context and behavior information into LLMs. |
Xingzhu Wang; Erhan Zhang; Yiqun Chen; Jinghan Xuan; Yucheng Hou; Yitong Xu; Ying Nie; Shuaiqiang Wang; Dawei Yin; Jiaxin Mao; |
| 130 | Transformers Are Good Clusterers for Lifelong User Behavior Sequence Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Given that centroids in clustering group similar data points based on proximity, similar to how queries function in transformers, we can integrate the learning of queries with CTR tasks in an end-to-end manner, shifting clustering from meaningless Euclidean distances to meaningful semantic distances. Therefore, we propose C-Former, a transformer-based clustering model specifically designed for modeling lifelong behavior sequences. |
Xingmei Wang; Shiyao Wang; Wuchao Li; Jiaxin Deng; Song Lu; Defu Lian; Guorui Zhou; |
| 131 | Bridging Queries and Tables Through Entities in Open-Domain Table Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we explore how to leverage entities in tables to improve retrieval performance. |
Da Li; Keping Bi; Jiafeng Guo; Xueqi Cheng; |
| 132 | Robust Heterogeneous GNNs Via Semantic Attention and Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although previous studies have preliminarily revealed the impact of structural perturbations on HGNN performance, there has been insufficient exploration into how to develop efficient defense mechanisms from both structural and semantic perspectives. To address this, we propose a novel defense framework that integrates a meta-path-guided semantic-aware attention mechanism. |
Chongjie Zhao; Jinyan Wang; Linlin Su; Zeming Gan; Ziyang Zhou; |
| 133 | You Only Evaluate Once: A Tree-based Rerank Method at Meituan Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These methods essentially involve making trade-offs between effectiveness and efficiency, while suffering from a severe inconsistency problem, that is, the GSU often misses high-value lists from ESU. To address this problem, we propose YOLOR, a one-stage reranking method that removes the GSU while retaining only the ESU. |
Shuli Wang; Yinqiu Huang; Changhao Li; Yuan Zhou; Yonggang Liu; Yongqiang Zhang; Yinhua Zhu; Haitao Wang; Xingxing Wang; |
| 134 | GReF: A Unified Generative Framework for Efficient Reranking Via Ordered Multi-token Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a Unified Generative Efficient Reranking Framework (GReF) to address the two primary challenges. |
Zhijie Lin; Zhuofeng Li; Chenglei Dai; Wentian Bao; Shuai Lin; Enyun Yu; Haoxiang Zhang; Liang Zhao; |
| 135 | Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, directly adapting general-purpose LLMs to log analysis using raw logs may degrade their performance due to inconsistent token distribution. In this paper, we present a domain adaptation approach that addresses these limitations by integrating interpretable domain knowledge into open-source LLMs through continual pre-training (CPT), which bridges this domain gap by adapting LLMs on interpretable natural texts with log knowledge (instead of raw logs) to reduce distribution discrepancy. |
Yuhe Ji; Yilun Liu; Feiyu Yao; Minggui He; Shimin Tao; Xiaofeng Zhao; Chang Su; Xinhua Yang; Weibin Meng; Yuming Xie; Boxing Chen; Shenglin Zhang; Yongqian Sun; |
| 136 | Benefit from Rich: Tackling Search Interaction Sparsity in Search Enhanced Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our idea is to leverage the features of users with rich search interactions to enhance those of users with sparse search interactions. Based on this idea, we propose GSERec, a method that utilizes message passing on the User-Code Graphs to alleviate data sparsity in Search-Enhanced Recommendation. |
Teng Shi; Weijie Yu; Xiao Zhang; Ming He; Jianping Fan; Jun Xu; |
| 137 | STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current models struggle with ensuring the validity and generalizability of inferred spatio-temporal patterns, especially in capturing dynamic spatial dependencies and temporal shifts, and optimizing the generalizability of unknown sensors. To overcome these limitations, we propose Spatio-Temporal Aware Graph Adversarial Neural Network (STA-GANN), a novel GNN-based kriging framework that improves spatio-temporal pattern validity and generalization. |
Yujie Li; Shao Zezhi; Chengqing Yu; Tangwen Qian; Zhao Zhang; Yifan Du; Shaoming He; Fei Wang; Yongjun Xu; |
| 138 | Calibrated and Diverse News Coverage Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To avoid bias, it has become common practice that news aggregators provide articles based on source diversity: for each story, they pick articles from news sources with different political leanings. In this paper, we ask whether this practice is sufficient. |
Tianyi Zhou; Stefan Neumann; Kiran Garimella; Aristides Gionis; |
| 139 | RankMixer: Scaling Up Ranking Models in Industrial Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce RankMixer, a hardware-aware model design tailored towards a unified and scalable feature-interaction architecture. |
Jie Zhu; Zhifang Fan; Xiaoxie Zhu; Yuchen Jiang; Hangyu Wang; Xintian Han; Haoran Ding; Xinmin Wang; Wenlin Zhao; Zhen Gong; Huizhi Yang; Zheng Chai; Zhe Chen; Yuchao Zheng; Qiwei Chen; Feng Zhang; Xun Zhou; Peng Xu; Xiao Yang; Di Wu; Zuotao Liu; |
| 140 | FEDDGCN: A Frequency-Enhanced Decoupling Dynamic Graph Convolutional Network for Traffic Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing approaches are still limited by the insufficient capability for spatial-temporal pattern decoupling and the underutilization of frequency domain information. To address these issues, we propose a novel Frequency-Enhanced Dynamic Decoupling Graph Convolutional Network (FEDDGCN), which introduces a gated decoupling mechanism integrating temporal and spatial embeddings to decouple traffic flow into prominent periodic and perturbative component. |
Wendong Zhang; Ruobai Xiang; Zhifang Liao; Peng Lan; Qihao Liang; |
| 141 | Unlocking The Potential of Smaller Language Models As Superior Instruction Evolvers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current methodologies for constructing large-scale instruction datasets tend to favor powerful models, such as GPT-4, based on the empirical assumption that larger models inherently possess superior capabilities. In this study, we challenge this prevailing assumption and delve into the untapped potential of smaller language models (SLMs) in the context of instruction evolution. |
Tingfeng Hui; Lulu Zhao; Guanting Dong; Yaqi Zhang; Sen Su; |
| 142 | MMFair: Fair Learning Via Min-Min Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In particular, improper fine-tuning on limited data often leads to overfitting, reinforcing spurious correlations and further undermining group fairness. To address this, we propose MMFair, an algorithm that optimizes perturbations through a min-min optimization approach. |
Kejie Fang; Kun Zhai; Xingjun Ma; |
| 143 | Pedagogy-R1: Pedagogical Large Reasoning Model and Well-balanced Educational Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing large language models (LLMs) often struggle to deliver instructional coherence, formative feedback, or simulate sophisticated teacher decision-making, limiting their practical utility in educational settings. To fill this gap, we present Pedagogy-R1, a comprehensive pedagogical reasoning framework designed to adapt LLMs for authentic classroom tasks. |
Unggi Lee; Jaeyong Lee; Jiyeong Bae; Yeil Jeong; Junbo Koh; Gyeonggeon ‘Boaz’ Lee; Gunho Lee; Taekyung Ahn; Hyeoncheol Kim; |
| 144 | DistillCaps: Enhancing Audio-Language Alignment in Captioning Via Retrieval-Augmented Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose DistillCaps, a novel training-time framework that leverages RAG to guide knowledge distillation for improved audio-language alignment, while lessening the reliance on retrieval during inference. |
Thinh Pham; Nghiem Diep; Lizi Liao; Binh T. Nguyen; |
| 145 | Adaptive Spike Neural Networks for Natural Language Inference Tasks with Dynamic Spike Predictor Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To improve learning efficiency and stability, we propose Dynamic Spike Predictor (DSP) that adaptively regulates spike generation. |
Seung-Kyu Hong; Hyuk-Yoon Kwon; |
| 146 | Empowering Denoising Sequential Recommendation with Large Language Model Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, we find that simply relying on collaborative information may result in an over-denoising problem, especially for cold items. To overcome these limitations, we propose a novel framework: Interest Alignment for Denoising Sequential Recommendation (IADSR) which integrates both collaborative and semantic information. |
Tongzhou Wu; Yuhao Wang; Maolin Wang; Chi Zhang; Xiangyu Zhao; |
| 147 | Social Relation Meets Recommendation: Augmentation and Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Engaging these users is crucial for platform growth. To bridge this gap, we propose leveraging the social-relation graph to enrich interest representations from behavior-based models. |
Lin Wang; Weisong Wang; Xuanji Xiao; Qing Li; |
| 148 | Enhancing Multimodal Entity Linking Via Distillation and Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although general-purpose multimodal large language models (MLLMs) are powerful, it is costly and time-consuming to apply them directly to the MEL task. To address the above issues, we propose a Distillation-Enhanced framework for Multimodal Entity Linking (DEMEL). |
Jintao Huang; Dong Wang; Shasha Li; Yuanxi Peng; Ruochun Jin; |
| 149 | Personalized Federated Recommendation with Multi-Faceted User Representation and Global Consistent Prototype Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose FedMUR, a novel federated recommendation framework that models user representation as a Gaussian mixture distribution, capturing users’ multi-faceted characteristics. |
Jiaming Qian; Xinting Liao; Xiangmou Qu; Zhihui Fu; Xingyu Lou; Changwang Zhang; Pengyang Zhou; Zijun Zhou; Jun Wang; Chaochao Chen; |
| 150 | LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we propose LLM4CD, which Leverages Large Language Models for open-world knowledge Augmented Cognitive Diagnosis. |
Weiming Zhang; Lingyue Fu; Qingyao Li; Kounianhua Du; Jianghao Lin; Jingwei Yu; Wei Xia; Weinan Zhang; Ruiming Tang; Yong Yu; |
| 151 | FunLoc: A Novel Function-level Bug Localization Framework Enhanced By Contrastive and Active Learning Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the critical challenges of handling domain-specific bug reports and managing vast function-level sample space, we introduce two key innovations that are seamlessly integrated into FunLoc. |
Ziye Zhu; Liangliang Peng; Yu Wang; Yun Li; Xianzhong Long; |
| 152 | SAKG: Structure-Aware Large Language Model Framework for Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing approaches to leveraging knowledge graphs in large language models often lack explicit structural modeling, which can lead to hallucinations and unstable reasoning over graph data. To address this, we propose SAKG, a structure-aware prompting framework designed to enhance the alignment between knowledge graph representations and language model inference. |
Qingyu Zhang; Min Hu; Wenlong Fei; Jiaoyun Yang; Hongbo Li; |
| 153 | Relation-Sensitive Visual Aggregation Enhances Multimodal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing multimodal knowledge graph completion methods often overlook triple correlations between relations and images, limiting the expressiveness of multimodal embeddings. In this paper, we categorize triple correlations into Intra-triple Correlations (IaC) and Inter-triple Correlations (IeC), and propose a method called Relation-Sensitive Visual Aggregation (RSVA) to explicitly model them. |
Qingyu Zhang; Min Hu; Wenlong Fei; Jiaoyun Yang; Hongbo Li; |
| 154 | Advancing Temporal Sensitive Question Answering Through Progressive Multi-Step Reflection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Motivated by the observed limitations, we propose ChronoReflect+, a temporal logic-aware RAG framework that incorporates hybrid temporal-aware retrieval and progressive multi-step reflection. |
Ziyang Chen; Erxue Min; Xiang Zhao; Yunxin Li; Xin Jia; Jinzhi Liao; Shuaiqiang Wang; Baotian Hu; Dawei Yin; |
| 155 | Adapting LLMs for Personalized Evaluation of Explanations for Recommendations: A Meta-Learning Approach Based on MAML Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although large language models (LLMs) have been used for automated evaluation of explanations, existing approaches fail to account for the highly personalized na- ture of explanation assessment, where user judgments towards the same explanations vary significantly. To address this, we pro- pose MAML+PEFT method that combines Model-Agnostic Meta- Learning (MAML) with LoRA-based parameter-efficient tuning to adapt LLMs for personalized explanation evaluation. |
Yurou Zhao; Yingfei Zhang; Quan Zhou; Shuang Zhang; Wei Lin; Jiaxin Mao; |
| 156 | C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing hallucination benchmarks (especially in Chinese language) rely on human annotations, making automatical and cost-effective hallucination evaluation challenging. To address this, we introduce HaluAgent, an agentic framework that automatically constructs fine-grained question-answering (QA) dataset based on some knowledge documents. |
Xu Zhang; Zhifei Liu; Jiahao Wang; Huixuan Zhang; Fan Xu; Junzhe Zhang; Xiaojun Wan; |
| 157 | Retrieval-LTV: Fine-Grained Transfer Learning for Lifetime Value Estimation in Large-Scale Industrial Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Incorporating rich data from other domains can mitigate the sparsity while introducing the negative transfer issue. To tackle these challenges, we introduce Retrieval-LTV, a two-tower retrieval model for LTV prediction. |
Shirui Wang; Shengbin Jia; Tianyue Cao; Shuo Yang; Lei Jiang; Qi He; Lingling Yao; Yang Xiang; |
| 158 | Publicly Verifiable and Fault-Tolerant Privacy-Preserving Aggregation for Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a novel collusion-resistant symmetric masking technique to conceal users’ local gradients while ensuring the correctness of the aggregated model through a publicly verifiable aggregation signature algorithm. |
Guohao Li; Qi Jiang; Lu Zhou; Li Yang; |
| 159 | Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present SIDSL (Structure-prior Informed Diffusion model for Source Localization), a generative diffusion framework that leverages topology-aware priors to enable robust source localization with limited data. |
Hongyi Chen; Jingtao Ding; Xiaojun Liang; Yong Li; Xiao-Ping Zhang; |
| 160 | Towards Fully-Automated Materials Discovery Via Large-Scale Synthesis Dataset and Expert-Level LLM-as-a-Judge Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To enable scalable evaluation, we propose an LLM-as-a-Judge framework that leverages large language models for automated assessment, demonstrating strong agreement with expert evaluations (e.g., Pearson’s r = 0.80, Spearman’s ρ = 0.78). |
Heegyu Kim; Taeyang Jeon; Seungtaek Choi; Ji Hoon Hong; Dong Won Jeon; Ga-Yeon Baek; Gyeong-Won Kwak; Dong-Hee Lee; Jisu Bae; Chihoon Lee; Yoon-Seo Kim; Seon-Jin Choi; Jin-Seong Park; Sung Beom Cho; Hyunsouk Cho; |
| 161 | HIT Model: A Hierarchical Interaction-Enhanced Two-Tower Model for Pre-Ranking Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we propose the Hierarchical Interaction-Enhanced Two-Tower (HIT) model, a new architecture that augments the two-tower paradigm with two key components: generators that pre-generate holistic vectors incorporating coarse-grained user-ad interactions through a dual-generator framework with a cosine-similarity-based generation loss as the training objective, and multi-head representers that project embeddings into multiple latent subspaces to capture fine-grained, multi-faceted user interests and multi-dimensional ad attributes. |
Haoqiang Yang; Congde Yuan; Kun Bai; Mengzhuo Guo; Wei Yang; Chao Zhou; |
| 162 | T-Stars-Poster: A Framework for Product-Centric Advertising Image Design Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods mainly focus on parts of the problem and lack a comprehensive solution. To bridge this gap, we propose a novel product-centric framework for advertising image design called T-Stars-Poster. |
Hongyu Chen; Min Zhou; Jing Jiang; Jiale Chen; Yang Lu; Zihang Lin; Bo Xiao; Tiezheng Ge; Bo Zheng; |
| 163 | Meta-Adaptive Network for Effective Cold-Start Recommendation Via Warm-Aware Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a Meta-Adaptive Network for Effective Cold-Start Recommendation (MANE). |
Ao Zhang; Boya Du; Yulin Xu; Jialin Zhu; Yuning Jiang; |
| 164 | Subclass-Aware Inclusive Classifier Via Repulsive Hidden Strata Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, imbalanced subclass distributions lead to majority subclasses dominating training, resulting in biased and less reliable models, especially for safety-critical applications (such as medical). To address these challenges, we propose a novel framework that attempts to uncover hidden subclasses via a repulsive point process. |
Namita Bajpai; Jiaul H Paik; Sudeshna Sarkar; |
| 165 | DP-COMET: A Differential Privacy Contextual Obfuscation MEchanism for Texts in Natural Language Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current privacy-preserving obfuscation mechanisms based on epsilon-Differential Privacy (DP) produce an obfuscated private text, changing the original phrase term-by-term without considering the context in which such a term is placed. This paper introduces DP-COMET, an epsilon-DP obfuscation mechanism that evaluates a text’s context before producing its private version. |
Francesco Luigi De Faveri; Guglielmo Faggioli; Nicola Ferro; |
| 166 | Study on LLMs for Promptagator-Style Dense Retriever Training Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we study the impact of open-source LLMs at accessible scales (≤14B parameters) as an alternative. |
Daniel Gwon; Nour Jedidi; Jimmy Lin; |
| 167 | Calibrating on Kolmogorov-Arnold Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we systematically examine the impact of four critical hyperparameters — Layer Width, Grid Order, Shortcut Function, and Grid Range — on the calibration of KANs. |
Wenhao Liang; Wei Emma Zhang; Lin Yue; Miao Xu; Olaf Maennel; Weitong Chen; |
| 168 | Calibrating on Medical Segmentation Model Through Signed Distance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce three contributions that jointly address spatial precision and reliability. |
Wenhao Liang; Wei Emma Zhang; Lin Yue; Miao Xu; Olaf Maennel; Weitong Chen; |
| 169 | MedSEBA: Synthesizing Evidence-Based Answers Grounded in Evolving Medical Literature Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These evolving studies can reach differing conclusions, which is not reflected in traditional search tools. To address these challenges, we introduce MedSEBA, an interactive AI-powered system for synthesizing evidence-based answers to medical questions. |
Juraj Vladika; Florian Matthes; |
| 170 | Empowering Large Language Model for Sequential Recommendation Via Multimodal Embeddings and Semantic IDs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These issues dampen the model scalability and lead to suboptimal recommendation performance. Therefore, based on LLMs like Llama3-8B-instruct, we introduce a novel SR framework named MME-SID, which integrates multimodal embeddings and quantized embeddings to mitigate embedding collapse. |
Yuhao Wang; Junwei Pan; Xinhang Li; Maolin Wang; Yuan Wang; Yue Liu; Dapeng Liu; Jie Jiang; Xiangyu Zhao; |
| 171 | Parse-LLM: A Prior-Free LLM Parser for Unknown System Logs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent advances have leveraged Large Language Models (LLMs) to handle log format complexities and enhance parsing performance. |
Chengyu Song; Lin Yang; Jianming Zheng; Jinzhi Liao; Feng Yang; Linru Ma; Fei Cai; |
| 172 | CSMD: Curated Multimodal Dataset for Chinese Stock Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the currently available resources are mainly based on the U.S. stock market in English, which is inapplicable to adapt to other countries. To address these issues, we propose CSMD, a multimodal dataset curated specifically for analyzing the Chinese stock market with meticulous processing for validated quality. |
Yu Liu; Zhuoying Li; Ruifeng Yang; Fengran Mo; Cen Chen; |
| 173 | Latent Variable Modeling for Robust Causal Effect Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In causal inference, they help account for hidden factors influencing treatment or outcome, thereby addressing challenges posed by missing or unmeasured covariates. This paper proposes a new framework that integrates latent variable modeling into the double machine learning (DML) paradigm to enable robust causal effect estimation in the presence of such hidden factors. |
Tetsuro Morimura; Tatsushi Oka; Yugo Suzuki; Daisuke Moriwaki; |
| 174 | InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most of the existing methods suffer from two fundamental limitations, including (1) insufficient inter-mode interaction due to the unidirectional information flow between modes, and (2) aggressive information aggregation caused by early summarization, resulting in excessive information loss. To address these limitations, we propose a novel module named InterFormer to learn heterogeneous information interaction in an interleaving style. |
Zhichen Zeng; Xiaolong Liu; Mengyue Hang; Xiaoyi Liu; Qinghai Zhou; Chaofei Yang; Yiqun Liu; Yichen Ruan; Laming Chen; Yuxin Chen; Yujia Hao; Jiaqi Xu; Jade Nie; Xi Liu; Buyun Zhang; Wei Wen; Siyang Yuan; Hang Yin; Xin Zhang; Kai Wang; Wen-Yen Chen; Yiping Han; Huayu Li; Chunzhi Yang; Bo Long; Philip S. Yu; Hanghang Tong; Jiyan Yang; |
| 175 | ECG-Doctor: An Interpretable Multimodal ECG Diagnosis Framework Based on Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large Language Models (LLMs) offer potential in low-data scenarios and generating interpretable outputs, yet their application to ECG diagnosis, especially leveraging multimodal data (e.g., raw signals, derived features, and clinical knowledge), remains underexplored. To address these challenges, we propose ECG-Doctor, an interpretable and multimodal ECG diagnosis framework based on LLMs. |
Dongsheng Tian; Junzhe Jiang; Kai Zhang; Changchun Liu; Yu Yuan; Min Gao; Enhong Chen; |
| 176 | Prompt Tuning As User Inherent Profile Inference Machine Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, LLMs face challenges like unstable instruction compliance, modality gaps, and high inference latency, leading to textual noise and limiting their effectiveness in recommender systems. To address these challenges, we propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. |
Yusheng Lu; Zhaocheng Du; Xiangyang Li; Pengyue Jia; Yejing Wang; Weiwen Liu; Yichao Wang; Huifeng Guo; Ruiming Tang; Zhenhua Dong; Yongrui Duan; Xiangyu Zhao; |
| 177 | Ensemble Pruning Via Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address this limitation, in this work, we model the base learners in an ensemble as a weighted and attributed graph, where node features represent characteristics of each learner and edge weights represent relationships between the base learners. Leveraging this representation, we propose a novel ensemble pruning method based on graph neural networks (GNNs). |
Yuanke Li; Yiyang Liu; Dongmian Zou; Hongfei Wang; |
| 178 | Compare: A Framework for Scientific Comparisons Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Navigating the vast and rapidly increasing sea of academic publications to identify institutional synergies, benchmark research contributions and pinpoint key research contributions has become an increasingly daunting task, especially with the current exponential increase in new publications. |
Moritz Staudinger; Wojciech Kusa; Matteo Cancellieri; David Pride; Petr Knoth; Allan Hanbury; |
| 179 | Augmenting Guest Search Results with Recommendations at Airbnb Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our solution introduces two key innovations: (1) a modular and extensible architecture to generate the flexible pivot recommendations that integrates seamlessly with Airbnb’s existing search ranking system, enabling rapid iteration and minimizing maintenance overhead; and (2) an efficient approach leveraging transfer learning and a Mixture of Experts (MoE) architecture to rank recommendations alongside organic search results. |
Haowei Zhang; Philbert Lin; Dishant Ailawadi; Soumyadip Banerjee; Shashank Dabriwal; Hao Li; Kedar Bellare; Liwei He; Sanjeev Katariya; |
| 180 | TAGA: Text-Attributed Graph Self-Supervised Learning By Synergizing Graph and Text Mutual Transformations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite the potential for deeper insights, existing TAG representation learning primarily omit the semantic relationship among node texts, and mostly relies on supervised methods, necessitating extensive labeled data and limiting applicability across diverse contexts. This paper introduces a new self-supervised learning framework, Text-Attributed-Graph Multi-View Alignment (TAGA), which overcomes these constraints by integrating TAGs’ structural and semantic dimensions. |
Zheng Zhang; Yuntong Hu; Bo Pan; Chen Ling; Liang Zhao; |
| 181 | Transferable Deep Clustering Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel transferable deep clustering model that can automatically adapt the cluster centroids according to the distribution of data samples. |
Zheng Zhang; Liang Zhao; |
| 182 | CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Universally incorporating neighborhood information is nontrivial due to the intrinsic complexity of traffic flow interactions, as well as the challenge of modeling collective influences from neighbor intersections. To address these challenges, we propose CityLight, which learns a universal policy based on representations obtained with two major modules: a Neighbor Influence Encoder to explicitly model neighbor’s influence with specified traffic flow relation and connectivity to the ego intersection; a Neighbor Influence Aggregator to attentively aggregate the influence of neighbors based on their mutual competitive relations. |
Jinwei Zeng; Chao Yu; Xinyi Yang; Wenxuan Ao; Qianyue Hao; Jian Yuan; Yong Li; Yu Wang; Huazhong Yang; |
| 183 | STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for Graph Neural Network (GNN) training and prediction. |
Amirhossein Ghaffari; Huong Nguyen; Lauri Lov\'{e}n; Ekaterina Gilman; |
| 184 | MARM: Unlocking The Recommendation Cache Scaling-Law Through Memory Augmentation and Scalable Complexity Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: During online inference, we must respond within milliseconds (LLMs usually take a few seconds). Considering the above differences with LLM, we can conclude that: for a RecSys model, compared to model parameters, the FLOPs is a more expensive factor that requires careful control.In this paper, we propose our milestone work, MARM (Memory Augmented Recommendation Model), which explores a new cache scaling-law successfully. |
Xiao Lv; Jiangxia Cao; Shijie Guan; Xiaoyou Zhou; Zhiguang Qi; Yaqiang Zang; Ben Wang; Guorui Zhou; |
| 185 | In-context Pre-trained Time-Series Foundation Models Adapt to Unseen Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing foundation models are typically pre-trained to enhance performance on specific tasks and often struggle to generalize to unseen tasks without fine-tuning. To address this limitation, we propose augmenting TSFMs with In-Context Learning (ICL) capabilities, enabling them to perform test-time inference by dynamically adapting to input-output relationships provided within the context. |
Shangqing Xu; Harshavardhan Kamarthi; Haoxin Liu; B. Aditya Prakash; |
| 186 | DSETA: Driving Style-Aware Estimated Time of Arrival Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Since different drivers may prefer specific routes and speeds based on their experience and familiarity with traffic conditions, driving styles play a crucial role in determining the actual ETA. To fill this gap, we present a novel approach, DSETA, which leverages deep learning to learn and then integrate driving style representations for personalized and precise ETA predictions. |
Bolin Zhang; Zhidan Liu; |
| 187 | TableTime: Reformulating Time Series Classification As Training-Free Table Understanding with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite effectiveness, we highlight three limitations that these methods overlook: (1) they struggle to incorporate temporal and channel-specific information, both of which are essential components of multivariate time series; (2) aligning the learned representation space with the semantic space of the LLMs proves to be a significant challenge; (3) they often require task-specific retraining, preventing training-free inference despite the generalization capabilities of LLMs. To bridge these gaps, we propose TableTime, which reformulates MTSC as a table understanding task. |
Jiahao Wang; Mingyue Cheng; Qingyang Mao; Yitong Zhou; Daoyu Wang; Qi Liu; Feiyang Xu; Xin Li; |
| 188 | SEF-UQR: Scalable and Efficient Privacy-Preserving Federated Updating QR Factorization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, privacy constraints prevent direct data sharing among participants, making collaborative QR decomposition updates challenging. To address this, we present SEF-UQR, a scalable and efficient framework for federated QR updates that focuses on incremental row-addition updates-common in streaming-data scenarios-while leveraging homomorphic encryption and interactive ciphertext protocols to protect both inputs and intermediate computations. |
Haonan Yuan; Wenyuan Wu; Jingwei Chen; |
| 189 | TCFMamba: Trajectory Collaborative Filtering Mamba for Debiased Point-of-Interest Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this task remains challenged by issues such as popularity bias, exposure bias, and limited representational capacity, all of which impede the accurate modeling of users and POIs, thereby restricting balanced and effective recommendations. Therefore, we propose Trajectory Collaborative Filtering Mamba (TCFMamba), which integrates two specially designed modules, i.e., Joint Learning of Static and Dynamic Representations (JLSDR) and Preference State Mamba Network (PSMN), for debiased Point-of-Interest recommendation. |
Jin Qian; Shiyu Song; Xin Zhang; Dongjing Wang; He Weng; Haiping Zhang; Dongjin Yu; |
| 190 | Lead-LagNet: Exploiting Lead-Lag Dependencies for Cross-Series Temporal Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, stacking GNN layers to capture multi-hop influences reduces interpretability, hindering understanding of underlying dynamics. To address these issues, we propose the Lead-LagNet, a framework designed to capture diverse cross-series propagation patterns with lead-lag phenomenon in time series. |
Zhilong Xie; Shaofei Shen; Jiwen Huang; Rui Cheng; Qing Li; |
| 191 | FedGVD: Efficient Federated Graph Learning Via Unidirectional Distillation with Dynamic Virtual Nodes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods face two major bottlenecks: the structural heterogeneity discrepancy of graph data among clients weakens the generalization ability of the global model; and model heterogeneity leads to inefficient knowledge sharing and complex global aggregation. To address these issues, we propose FedGVD, an efficient framework that constructs a global perspective through data condensation and server-side virtual node generation, which not only preserves the semantic equivalence of the original data but also avoids privacy leakage. |
Zhehao Dai; Guojiang Shen; Yuyue Hu; Jiaxin Du; Xiao Han; Xiangjie Kong; |
| 192 | Decoder-only Pre-training Enhancement for Spatio-temporal Traffic Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To eliminate this gap, we propose a new pre-training paradigm named next patch prediction and prove its advantages from both empirical and theoretical perspectives. Based on this paradigm, we introduce a new framework called Decoder-only Pre-training Enhancement (DoP) to unleash the potential of traffic pre-training model. |
Tao Yu; Junhong Wan; Yao Fu; Weihao Jiang; Jiang Zhu; |
| 193 | Advanced News Event Clustering Via Topic Enhanced Modeling with Multi-Aspect Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: News event clustering, a crucial task for discovering and comprehending real-world information, aims to aggregate news articles into fine-grained clusters based on specific key events. |
Hang Yang; Xiaoyan Yu; Dianbo Sui; |
| 194 | Fact or Facsimile? Evaluating The Factual Robustness of Modern Retrievers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We pair 12 publicly released embedding checkpoints with their original base LLMs and evaluate both sets on a factuality benchmark. |
Haoyu Wu; Qingcheng Zeng; Kaize Ding; |
| 195 | CLAP: Coreference-Linked Augmentation for Passage Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Coreference-Linked Augmentation for Passage Retrieval (CLAP), a lightweight LLM-based expansion framework that segments passages into coherent chunks, resolves coreference chains, and generates localized pseudo-queries aligned with dense retriever representations. |
Huanwei Xu; Lin Xu; Liang Yuan; |
| 196 | Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: The core insight is that by considering news intents, one can deeply understand the inherent thoughts behind news deception, rather than the surface patterns within words alone. To achieve this goal, we propose Graph-based INtent-Semantic joInt moDEling (InSide) for fake news detection, which models deception clues from both semantic and intent signals via graph-based joint learning. |
Zhengjia Wang; Qiang Sheng; Danding Wang; Beizhe Hu; Juan Cao; |
| 197 | Selective Mixup for Debiasing Question Selection in Computerized Adaptive Testing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, the imbalance in examinees’ historical interactions often exacerbates bias in diagnostic models. To address this issue, we propose a debiasing framework consisting of two key modules: Cross-Attribute Examinee Retrieval and Selective Mixup-based Regularization. |
Mi Tian; Kun Zhang; Fei Liu; Jinglong Li; Yuxin Liao; Chenxi Bai; Zhengtao Tan; Le Wu; Richang Hong; |
| 198 | Personalized Tree-Based Progressive Regression Model for Watch-Time Prediction in Short Video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Thus, we propose PTPM to enable highly personalized decomposition of watch estimation with better efficacy and efficiency. |
Xiaokai Chen; Xiao Lin; Changcheng Li; Peng Jiang; |
| 199 | Improving Rare and Common ICD Coding Via A Multi-Agent LLM-Based Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing LLM-based approaches typically fail to capture the complex, dynamic interactions among human agents involved in real-world coding workflows-such as patients, physicians, and coders-and often lack interpretability and reliability. To address these challenges, we propose a novel multi-agent framework for ICD coding that simulates the real-world process using five role-specific LLM agents-patient, physician, coder, reviewer, and adjuster-and integrates the Subjective, Objective, Assessment, and Plan (SOAP) structure from Electronic Health Records to enhance performance. |
Rumeng Li; Xun Wang; Hong Yu; |
| 200 | Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Enhancing the mathematical reasoning of large language models (LLMs) demands high-quality training data, yet conventional methods face critical challenges in scalability, cost, and data reliability. To address these limitations, we propose a novel program-assisted synthesis framework that systematically generates a high-quality mathematical corpus with guaranteed diversity, complexity, and correctness. |
Sirui Chen; Changxin Tian; Binbin Hu; Kunlong Chen; Ziqi Liu; Zhiqiang Zhang; Jun Zhou; |
| 201 | Channel-Independent Refiner for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In order to learn channel patterns better, we propose a channel-independent Refiner as a plug-and-play module. |
Jie Wang; Zhongguang Zheng; Chaoliang Zhong; Jun Sun; |
| 202 | Integrating Time Series Into LLMs Via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose the Multi-layer Steerable Embedding Fusion (MSEF), a novel framework that enables LLMs to directly access time series patterns at all depths, thereby mitigating the progressive loss of TS information in deeper layers. |
Zhuomin Chen; Dan Li; Jiahui Zhou; Shunyu Wu; Haozheng Ye; Jian Lou; See-Kiong Ng; |
| 203 | STGS: Spatio-temporal Graph Sparsification Using Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce STGS (Spatio-Temporal Graph Sparsification), a reinforcement learning-based framework for sparsifying spatio-temporal graphs. |
Nasrin Shabani; Amin Beheshti; Yuankai Qi; Venus Haghighi; Jin Foo; Jia Wu; |
| 204 | A Hierarchical Structure-Enhanced Personalized Recommendation Model for Traditional Chinese Medicine Formulas Based on KG Diffusion Guidance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: (iii) The oversight of the ‘monarch, minister, assistant and envoy’ compatibility among herbs increases the risk of toxicity or side effects, opposing the ‘treatment based on syndrome differentiation’ principle in clinical TCM. Therefore, we propose a novel hierarchical structure-enhanced personalized recommendation model for TCM formulas based on knowledge graph (KG) diffusion guidance, namely TCM-HEDPR. |
Chaobo Zhang; Long Tan; |
| 205 | Harnessing Commonsense: LLM-Driven Knowledge Integration for Fine-Grained Sentiment Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent advancements leveraging large language models (LLMs) as data generators show promise but are limited by the LLMs’ lack of nuanced, domain-specific understanding and pose a significant risk of data leakage during inference, potentially leading to inflated performance metrics. To address these limitations, we propose LLM-Kit, a novel framework for commonsense-enhanced fine-grained sentiment analysis that integrates knowledge via LLM-guided graph construction, effectively mitigating data leakage risks. |
Kai Zhang; Yupeng Han; |
| 206 | Antelope: Potent and Concealed Jailbreak Attack Strategy Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current attack methodologies primarily encompass adversarial prompt engineering or concept obfuscation, yet they frequently suffer from slow search efficiency, conspicuous attack characteristics and poor alignment with targets. To overcome these challenges, we propose Antelope, a more robust and covert jailbreak attack strategy designed to expose security vulnerabilities inherent in generative models. |
Xin Zhao; Xiaojun Chen; Haoyu Gao; |
| 207 | Billion-Scale Graph Deep Learning Framework for Ads Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we systemically disentangle BHG, a graph deep learning framework for daily users’ ads recommendations. |
Si Zhang; Weilin Cong; Dongqi Fu; Andrey Malevich; Hao Wu; Baichuan Yuan; Xin Zhou; Kaveh Hassani; Zhigang Hua; Austin Derrow-Pinion; Yan Xie; Xuewei Wang; Yinglong Xia; Ning Yao; Vena Li; Sem Park; Bo Long; |
| 208 | PyLate: Flexible Training and Retrieval for Late Interaction Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite these compelling empirical results and clear theoretical advantages, the practical adoption and public availability of late interaction models remain low compared to their single-vector counterparts, primarily due to a lack of accessible and modular tools for training and experimenting with such models. To bridge this gap, we introduce PyLate, a streamlined library built on top of Sentence Transformers to support multi-vector architectures natively, inheriting its efficient training, advanced logging, and automated model card generation while requiring minimal code changes to code templates users are already familiar with. |
Antoine Chaffin; Rapha\{e}l Sourty; |
| 209 | Usefulness and Diminishing Returns: Evaluating Social Information in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Working towards answering the research questions, we introduce evaluation metrics to estimate the utilization of social information in the existing social recommendation models. |
Qing Meng; Huiyu Min; Ming Shan Hee; Roy Ka-Wei Lee; Bing Tian Dai; Shuai Xu; |
| 210 | Spatio-Temporal Forecasting Under Open-World Missingness with Adaptive Mixture-of-Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional methods universally overlook the dynamic nature of missingness, resulting in degraded predictive accuracy. To address this gap, we propose a novel Spatio-Temporal Missing-aware Mixture-of-Experts (STMMoE) architecture, equipped with a three-stage training strategy. |
Chenyu Wu; Zhipeng Ma; Junbo Zhang; Songyu Ke; Yu Zheng; |
| 211 | StepTool: Enhancing Multi-Step Tool Usage in LLMs Via Step-Grained Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose modeling tool learning as a dynamic decision-making process and introduce StepTool, a novel step-grained reinforcement learning framework that enhances LLMs’ capabilities in multi-step tool use. |
Yuanqing Yu; Zhefan Wang; Weizhi Ma; Shuai Wang; Chuhan Wu; Zhiqiang Guo; Min Zhang; |
| 212 | EduCraft: A System for Generating Pedagogical Lecture Scripts from Long-Context Multimodal Presentations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces EduCraft, a novel system designed to automate Lecture Script Generation (LSG), addressing key difficulties such as comprehensive multimodal understanding, long-context coherence, and instructional design efficacy. |
Yucheng Wang; Jifan Yu; Daniel Zhang-Li; Joy Jia Yin Lim; Shangqing Tu; Haoxuan Li; Zhiyuan Liu; Huiqin Liu; Lei Hou; Juanzi Li; Bin Xu; |
| 213 | Target Item-oriented Conditional Diffusion Differential Transformer for Next-Item Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, they often suffer from the problem of limited vector representation capability. To tackle the above two challenges, we propose a novel solution called target item-oriented conditional diffusion differential Transformer (ICDDT). |
Xiaoqing Chen; Zitao Xu; Weike Pan; Zhong Ming; |
| 214 | LGC-CR: Few-shot Knowledge Graph Completion Via Local Global Contrastive Learning and LLM-Guided Refinement Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a novel framework that combines meta-learning, enhanced via a Local-Global Contrastive network, with LLM-guided Contextual Refinement (LGC-CR). |
Yiming Xu; Qi Song; Yihan Wang; Wangqiu Zhou; Junli Liang; |
| 215 | MI4Rec: Pretrained Language Model Based Cold-Start Recommendation with Meta-Item Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This poses challenges in both learning accurate item token embeddings and generalizing efficiently to accommodate the continual influx of new items. In this work, we propose a novel meta-item token learning strategy to address both these challenges simultaneously. |
Zaiyi Zheng; Yaochen Zhu; Haochen Liu; Mingxuan Ju; Tong Zhao; Neil Shah; Jundong Li; |
| 216 | SarRec: Statistically-guaranteed Augmented Retrieval for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods typically retrieve items for each user without any principled mechanism for guaranteeing the reliability of generated recommendations, limiting their trustworthiness. To address this, we introduce SarRec : Statistically-guaranteed Augmented Retrieval for Recommendations, a framework that uses a simple retrieval step to provide relevant context and delivers calibrated, uncertainty-aware predictions with formal statistical guarantees. |
Tong Zhang; Nitin Bisht; Zihao Li; Guandong Xu; Xianzhi Wang; |
| 217 | An Robust Entity Alignment Method Based on Knowledge Distillation with Noisy Aligned Pairs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, a robust EA method based on knowledge distillation is proposed for noisy pairs. |
Yuhong Zhang; Hangchi Song; Xiaolong Zhu; Chenyang Bu; Kui Yu; |
| 218 | PP-STAT: An Efficient Privacy-Preserving Statistical Analysis Framework Using Homomorphic Encryption Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present PP-STAT, a novel and efficient Homomorphic Encryption (HE)-based framework for privacy-preserving statistical analysis. |
Hyunmin Choi; |
| 219 | Edge-Variational Graph Neural Networks: Harnessing Weak Ties for Enhanced Default Risk Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, in practical DRP implementations, the confidence in both strong and weak connections may be compromised due to fraudulent activities and misinformation caused by data collection biases, necessitating a novel method to effectively assess and calibrate low-confidence connections within the FRN to enhance DRP. To address this challenge, we propose a novel method named Edge-Variational Graph Neural Networks (EVGNN). |
Feng Zhang; Jianfeng Chi; Rui Ma; Gang Chen; Rongqi Chen; |
| 220 | To Know What User Concerns: Conceptual Knowledge Reasoning for User Satisfaction Estimation in E-Commerce Dialogue Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a knowledge-enhanced USE model named CoRe-USE, which introduces the Conceptual Knowledge Reasoning for USE in E-Commerce Dialogue Systems. |
Li Lin; Yaochang Liu; Kaiwen Xia; Shuai Wang; |
| 221 | Adaptive Context-Infused Performance Evaluator for Iterative Feature Space Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods commonly suffer from three major limitations: 1) ignoring differences between samples leads to evaluation bias; 2) the feature space is overly tailored to specific models, resulting in overfitting and poor generalization; and 3) retraining the evaluator from scratch in each iteration significantly reduces overall efficiency. To bridge these gaps, we introduce EASE (gEneralized Adaptive feature Space Evaluator), a generalized framework for efficient and objective evaluation of iteratively generated feature spaces. |
Yanping Wu; Yanyong Huang; Zijun Yao; Yanjie Fu; Kunpeng Liu; Xiao Luo; Dongjie Wang; |
| 222 | Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this fragmented architecture is brittle, expensive to maintain, and slow to adapt to evolving taxonomies and language patterns. In this paper, we introduce a unified query understanding framework powered by a Large Language Model (LLM), designed to address these limitations. |
Ping Liu; Jianqiang Shen; Qianqi Shen; Chunnan Yao; Kevin Kao; Dan Xu; Rajat Arora; Baofen Zheng; Caleb Johnson; Liangjie Hong; Jingwei Wu; Wenjing Zhang; |
| 223 | ECKGBench: Benchmarking Large Language Models in E-commerce Leveraging Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, leveraging a collected knowledge graph (KG) as a reliable source, we propose ECKGBench, a question-answering dataset to assess LLMs’ capacity in e-commerce. |
Langming Liu; Haibin Chen; Yuhao Wang; Yujin Yuan; Shilei Liu; Wenbo Su; Xiangyu Zhao; Bo Zheng; |
| 224 | Interpretable Meta-weighting Sparse Neural Additive Networks for Datasets with Label Noise and Class Imbalance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a novel Meta-weighted Sparse Neural Additive Model (MSpNAM), which offers robustness through an efficient bilevel weighting policy and inherits strong explainability and representation capabilities from the additive modeling strategy. |
Xuelin Zhang; Hong Chen; Lingjuan Wu; |
| 225 | Fraudulent Delivery Detection with Multimodal Courier Behavior Data in Last-Mile Delivery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present a Multimodal Fraudulent Delivery Detection framework (MFDD), which integrates heterogeneous data from multiple agents (courier-side and user-side)-including couriers’ physical behavior, digital behavior, and conversations containing customer feedback-for detecting fraudulent deliveries in the last-mile delivery. |
Shanshan Wang; Sijing Duan; Shuxin Zhong; Zhiqing Hong; Zhiyuan Zhou; Hongyu Lin; Weijian Zuo; Desheng Zhang; Yi Ding; |
| 226 | GCVPN: A Graph Convolutional Visual Prior-Transform Network for Actual Occluded Image Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Image recognition plays a critical role in urban security, traffic management, and environmental monitoring, yet achieving high accuracy in obstructed scenes remains a challenge. To address this, we propose a Graph Convolutional Visual Prior-Transform Network (GCVPN), which significantly improves recognition accuracy and efficiency in complex environments. |
Lei Wang; Nannan Wu; Huaming Wu; Wei Yu; Fan Zhang; Shuo Chen; |
| 227 | LLMCBR: Large Language Model-based Multi-View and Multi-Grained Learning for Bundle Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present a novel framework named Large Language Model-based Multi-View and Multi-Grained Learning for Bundle Recommendation (LLMCBR). |
Shiqin Liu; Chaozhuo Li; Minjun Zhao; Litian Zhang; Jiajun Bu; |
| 228 | Hyperspherical Dynamic Multi-Prototype with Arguments Dependencies and Role Consistency for Event Argument Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods face challenges in modeling intra-class variance and inter-class ambiguity, hindering accurate role assignment. Inspired by how humans dynamically adjust classification criteria while maintaining category consistency (e.g., distinguishing ”Victim” and ”Attacker” roles based on contextual relationships), we propose a HDMAR (Hyperspherical Dynamic Multi-Prototype with Arguments Dependencies and Role Consistency) method, where three innovations tackle these challenges: (1) Hyperspherical dynamic multi-prototype learning is used to capture intra-role diversity and enforce inter-role separation via hyperspherical optimization and optimal transport, (2) cross-event role consistency is used to align role representations across events, and (3) an arguments dependencies-guided encoding module enhances contextual understanding of intra-event and inter-event dependencies. |
Xiaojia Huang; Ruifang He; Fei Huang; Bo Wang; Sen Yao; Xiaohong Li; |
| 229 | A Cost-Effective Framework to Evaluate LLM-Generated Relevance Judgements Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our work introduces an innovative framework for estimating the quality of LLM-generated relevance judgments, providing statistical guarantees while minimizing human involvement. |
Simone Merlo; Stefano Marchesin; Guglielmo Faggioli; Nicola Ferro; |
| 230 | ACMCG: A Cost-effective Active Clustering with Minimal Constraint Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a newly designed Active Clustering framework with Minimal Constraint Graph (ACMCG). |
Qiu-Yu Wang; Wen-Bo Xie; Tao Deng; Tian Zou; Xuan-Lin Zhu; Xun Fu; Xin Wang; |
| 231 | SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods also often lack adaptive multi-source signal fusion tailored to item popularity. This paper introduces SPARK, a novel multi-stage framework systematically tackling these issues. |
Binhao Wang; Yutian Xiao; Maolin Wang; Zhiqi Li; Tianshuo Wei; Ruocheng Guo; Xiangyu Zhao; |
| 232 | TriSeRec: A Tri-view Representation Learning Framework for Sequential/Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Sequential/session-based recommendation models aim to learn evolving user preferences from historical user behaviors. |
Xinchen Yuan; Yichao Lu; |
| 233 | FinSage: A Multi-aspect RAG System for Financial Filings Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose the FinSage framework as a solution, utilizing a multi-aspect RAG framework tailored for data retrieval and summarization in multi-modal financial documents. |
Xinyu Wang; Jijun Chi; Zhenghan Tai; Tung Sum Thomas Kwok; Hailin He; Zhuhong Li; Yuchen Hua; Muzhi Li; Peng Lu; Suyucheng Wang; Yihong Wu; Huang Jerry; Jingrui Tian; Fengran Mo; Yufei Cui; Ling Zhou; |
| 234 | MHSNet: An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose MHSNet, an multi-level identity verification framework that fine-tunes BGE-M3 using contrastive learning. |
Yu Li; Zulong Chen; Wenjian Xu; Hong Wen; Yipeng Yu; Manlung Yiu; Yuyu Yin; |
| 235 | Hybrid2: Distributed GNN Training System Enhanced By Dual-Hybrid for Sampling and Loading Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present Hybrid2, a distributed GNN training system that combines full-graph and mini-batch training through a novel hybrid-batch training method. |
Chu Zhao; Shengjie Dong; Yuhai Zhao; Yuan Li; Zhengkui Wang; Xingwei Wang; |
| 236 | IPNet: An Interaction Pattern-aware Neural Network for Temporal Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel continuous-time model, the Interaction Pattern-aware neural Network (IPNet), to capture node-level behavior patterns and network evolution by encoding interaction sequences and contextual windows. |
Qingyang Zhang; Yitong Wang; Xinjie Lin; |
| 237 | Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This raises concerns about their reliability and applicability in real-world deployment. To address this, we present the Real-E dataset, covering over 74 power stations across 30+ European countries over a 10-year span with rich metadata. |
Chen Shao; Michael F\{a}rber; Sebastian P\{u}tz; Benjamin Sch\{a}fer; Yue Wang; Tobias K\{a}fer; Zhanbo Huang; Zhenyi Zhu; |
| 238 | LinkGPT: Leveraging Large Language Models for Enhanced Link Prediction in Text-Attributed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we evaluate an LLM’s ability to reason over structured data and infer new facts based on learned patterns by focusing on link prediction (LP)-the task of predicting missing links between nodes-that is understudied in the literature. |
Zhongmou He; Jing Zhu; Shengyi Qian; Joyce Chai; Danai Koutra; |
| 239 | MFAE: Multimodal Feature Adaptive Enhancement for Fake News Video Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these methods often fail to fully utilize the complex information across all modalities and overlook the potential for video manipulation, limiting their overall performance. To tackle these issues, MFAE is proposed, a novel framework for Multimodal Feature Adaptive Enhancement for Fake News Video Detection. |
Wenhao Wang; Mingxin Li; Jiao Qiao; Haotong Du; Xianghua Li; Chao Gao; Zhen Wang; |
| 240 | Leveraging Large Language Models for Complementary Product Ads Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, depending on the product types, identifying the complements of a given product may require extensive domain knowledge that is not present in a pair of complementary products. In this work, we propose a novel generate-and-retrieval paradigm to make complementary product recommendations and explore the use of LLMs for this task. |
Byung Eun Jeon; Ryan Bae; Xiao Bai; |
| 241 | Towards Unbiased and Real-Time Staytime Prediction for Live Streaming Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this introduces label truncation bias, distorting the unbiased estimation of high staytime samples. To reconcile these competing demands, we propose MS3M (Multi-Stream Segmented Staytime Modeling), a novel framework that leverages multiple data streams for faster learning while employing segmented staytime modeling-converting staytime regression into a series of time-segmented classification tasks to ensure unbiased training. |
Haiyuan Zhao; Changshuo Zhang; Yang Wang; Hao Wang; Zhen Ouyang; Bin Yuan; Qinglei Wang; Zuotao Liu; |
| 242 | Latent Graph Structure Learning for Large-Scale Traffic Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, since traffic states on road network are highly dynamic, current static and hard partition methods can not fully adapt to locations whose traffic semantics and evolving patterns vary across time. Therefore, we suppose locations on road network latently belong to certain graph structures and propose an adaptive and dynamic patching method in data-driven fashion. |
Meng Wang; Longgang Xiang; Chenhao Wu; Zejiao Wang; Xin Chen; Shaozu Xie; Ying Luo; |
| 243 | Learning from Graph: Mitigating Label Noise on Graph Through Topological Feature Reconstruction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we shift the focus to exploring how to extract useful information and learn from the graph itself, thus achieving robust graph learning. |
Zhonghao Wang; Yuanchen Bei; Sheng Zhou; Zhiyao Zhou; Jiapei Fan; Hui Xue; Haishuai Wang; Jiajun Bu; |
| 244 | Exploring The Tradeoff Between Diversity and Discrimination for Continuous Category Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, most of them use knowledge distillation and data replay to prevent forgetting, occupying more storage space. To address these limitations, we propose Independence-based Diversity and Orthogonality-based Discrimination (IDOD). |
Ruobing Jiang; Yang Liu; Haobing Liu; Yanwei Yu; Chunyang Wang; |
| 245 | ROKAN: Toward Interpretable and Domain-Robust Memory Behavior Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing approaches often struggle to balance predictive accuracy, domain generalization, and model interpretability. To address this, we propose ROKAN, a cognitively inspired and symbolically interpretable memory modeling framework. |
Xiaoxuan Shen; Zhihai Hu; Di Chen; Jianwen Sun; Shengyingjie Liu; |
| 246 | SC-DAG: Semantic-Constrained Diffusion Attacks for Stealthy Exposure Manipulation in Visually-Aware Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we present Semantic-Constrained Diffusion Adversarial Generation (SC-DAG) for visual shilling attacks. |
Ze Lin; Yuqiu Qian; Xiaodong Li; Ziyu Lyu; Hui Li; |
| 247 | QGCMA: A Framework for Knowledge-Based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current methodologies often grapple with the effective alignment of visual and textual features, as well as the utilization of structured knowledge bases, which limits their performance in handling intricate semantic and inferential tasks. To address these critical issues, this paper presents a framework based on three key innovations. |
Wei Li; Zhixin Li; |
| 248 | Leveraging Generative Models for Real-Time Query-Driven Text Summarization in Large-Scale Web Search Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we propose a novel framework to pioneer the application of generative models to address real-time QDTS in industrial web search. |
Zeyu Xiong; Yixuan Nan; Li Gao; Hengzhu Tang; Shuaiqiang Wang; Junfeng Wang; Dawei Yin; |
| 249 | AdaPatch: Adaptive Patch-Level Modeling for Non-Stationary Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Nonetheless, these methods operate at the instance level, assuming a homogeneous distribution across all time steps within an instance and relying on fixed statistical normalization. This limits their ability to effectively capture fine-grained distributional shifts.In this paper, we introduce AdaPatch, a novel forecasting model specifically designed to tackle non-stationary multivariate time series. |
Kun Liu; Zhongjie Duan; Cen Chen; Yanhao Wang; Dawei Cheng; Yuqi Liang; |
| 250 | Twin-Flow Generative Ranking Network for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, splitting user behaviors into interleaved item and action information significantly increases the input sequence length, which adversely affects both training and inference efficiency. To address this issue, we propose the Twin-Flow Generative Ranking Network (TFGR), that employs a Twin-flow mechanism to optimize interaction modeling, ensuring efficient training and inference through end-to-end token processing. |
Hao Guo; Erpeng Xue; Lei Huang; Shichao Wang; Xiaolei Wang; Lei Wang; Jinpeng Wang; Zeshun Li; Sheng Chen; |
| 251 | Mixture-of-KAN for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose the multi-layer mixture-of-KAN network, which achieves excellent performance while retaining KAN’s ability to be transformed into a combination of symbolic functions. |
Xiao Han; Zhenduo Zhang; Xinfeng Zhang; Yiling Wu; Zhe Wu; |
| 252 | MPFormer: Adaptive Framework for Industrial Multi-Task Personalized Sequential Retriever Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes the MPFormer, a dynamic multi-task Transformer framework, which systematically addresses the aforementioned issues through three innovative mechanisms. |
Yijia Sun; Shanshan Huang; Linxiao Che; Haitao Lu; Qiang Luo; Kun Gai; Guorui Zhou; |
| 253 | Taming Ultra-Long Behavior Sequence in Session-wise Generative Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this strategy is incompatible with generative frameworks due to their target-agnostic nature. To address these challenges, we propose a novel encoder-decoder model named HiCoGen (Hierarchical Compression-based Session-wise Generative Model), which efficiently models long-term interests in generative models. |
Wuchao Li; Shiyao Wang; Kuo Cai; Jiaxin Deng; Xingmei Wang; Qigen Hu; Defu Lian; Guorui Zhou; |
| 254 | Advancing Graph Isomorphism Tests with Metric Space Indicators: A Tool for Improving Graph Learning Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To enhance the capability of Graph Neural Networks (GNNs) in judging graph isomorphism and graph classification tasks, this paper introduces a metric space-based graph isomorphism judgment method called the k-MSI test, which offers more topological information than the k-WL test and demonstrates superior graph isomorphism judgment capabilities compared to the k-WL test at the same complexity level. |
Shenghui Zhang; Pak Lon Ip; Rongqin Chen; Shunran Zhang; Leong Hou U; |
| 255 | A Node-Aware Dynamic Quantization Approach for Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In the realm of collaborative filtering recommendation systems, Graph Neural Networks (GNNs) have demonstrated remarkable performance but face significant challenges in deployment on resource-constrained edge devices due to their high embedding parameter requirements and computational costs. Using common quantization method directly on node embeddings may overlooks their graph based structure, causing error accumulation during message passing and degrading the quality of quantized embeddings.To address this, we propose Graph based Node-Aware Dynamic Quantization training for collaborative filtering (GNAQ), a novel quantization approach that leverages graph structural information to enhance the balance between efficiency and accuracy of GNNs for Top-K recommendation. |
Lin Li; Chunyang Li; Yu Yin; Xiaohui Tao; Jianwei Zhang; |
| 256 | SUMMA: A Multimodal Large Language Model for Advertisement Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the effective utilization of multimodal information with high commercial value still largely constrained by reliance on highly compressed video embeddings-has long been inadequate. To address this, we propose SUMMA (the abbreviation of SUmmarizing MultiModalAds), a multimodal model that automatically processes video ads into summaries highlighting the content of highest commercial value, thus improving their comprehension and ranking in Douyin search-advertising systems. |
Weitao Jia; Shuo Yin; Zhoufutu Wen; Han Wang; Zehui Dai; Kun Zhang; Zhenyu Li; Tao Zeng; Xiaohui Lv; |
| 257 | Data-centric Prompt Tuning for Dynamic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, their exclusive focus on modifying node or temporal features while neglecting spatial structural information leads to limited expressiveness and degraded performance. To address these limitations, we propose DDGPrompt, a data-centric prompting framework designed to effectively refine pre-trained node embeddings at the input data level, enabling better adaptability to diverse downstream tasks. |
Yufei Peng; Cheng Yang; Zhengjie Fan; Chuan Shi; |
| 258 | DO: An Efficient Deep Reinforcement Learning Approach for Optimal Route with Collective Spatial Keywords Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing Point of Interest (POI) candidate set-based and path expansion-based methods frequently produce inferior route quality or excessive time overhead, particularly under large-scale query keywords. To address this challenge, we introduce the DO framework, which pioneers the employ Deep Reinforcement Learning for the ORCSK. |
Jiajia Li; Jiming Dong; Lei Li; Yu Yang; Xin Wang; Mengxuan Zhang; |
| 259 | Revisiting The Inner Product Method: Optimizing Sparse Matrix Multiplication Via Set Intersection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper investigates the strong connection between SpGEMM and set intersection computation, introducing a hybrid sparse matrix multiplication algorithm that builds upon the numerical computation of the IP method. |
Zheng Hu; Boyu Yang; Weiguo Zheng; |
| 260 | Personalized Multi Modal Alignment Encoding for CTR-Recommendation in WeChat Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: (2) User-irrelevant Clustering Assignment: For the specific item, most of existing quantization methods assume that all users share the same cluster assignments, failing to account for the varying interpretations and emotional responses users may have toward an item. To address these challenges, we propose a Personalized Multi Modal Alignment Encoding for CTR-Recommendation in WeChat (PMMAE for short). |
Jiawei Zheng; Hao Gu; Lingling Yi; Jie Wen; Chuan Chen; |
| 261 | GenR1-Searcher: Curriculum Reinforcement Learning for Dynamic Retrieval and Document Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose GenR1-Searcher, a curriculum-based reinforcement learning framework that enables small language models to intelligently decide between retrieval and document generation during multi-hop reasoning through a three-stage progressive training strategy: first learning tool invocation syntax through format rewards, then mastering retrieval strategies with answer-based rewards, and finally acquiring adaptive tool selection capabilities when both knowledge sources are available. |
Yu Wang; Yixuan Zhao; Renrui Duan; Jingyuan Li; Yuanzhuo Wang; Kun Zhang; |
| 262 | Robust Multi-Label Learning with Instance-Dependent Label Noise Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In contrast, existing methods designed for single-label IDN cannot be directly applied to multi-label data. To address these challenges, we propose Robust multi-label learning with Instance-Dependent label noisE (RIDE), a framework for multi-label learning with IDN. |
You Wu; Yabo Shi; Yizhang Zou; Peipei Li; |
| 263 | Towards Unveiling Predictive Uncertainty Vulnerabilities in The Context of The Right to Be Forgotten Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the vulnerabilities of such generated predictive uncertainties with regard to dedicated malicious unlearning attacks remain unexplored. To bridge this gap, for the first time, we propose a new class of malicious unlearning attacks against predictive uncertainties, where the adversary aims to cause the desired manipulations of specific predictive uncertainty results. |
Wei Qian; Chenxu Zhao; Yangyi Li; Wenqian Ye; Mengdi Huai; |
| 264 | Unplug and Play Language Models: Decomposing Experts in Language Models at Inference Time Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, recent studies have revealed that certain neurons play disproportionately important roles in solving specific tasks, suggesting that task-relevant substructures can be isolated and selectively activated for each task. Therefore, we introduce Decomposition of Experts (DoE), a novel framework that dynamically identifies and activates task-specific experts within a language model to reduce inference cost without sacrificing accuracy. |
Nakyeong Yang; Jiwon Moon; Junseok Kim; Yunah Jang; Kyomin Jung; |
| 265 | Exploring Causal Effect of Social Bias on Faithfulness Hallucinations in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Large language models (LLMs) have achieved remarkable success in various tasks, yet they remain vulnerable to faithfulness hallucinations, where the output does not align with the input. In this study, we investigate whether social bias contributes to these hallucinations, a causal relationship that has not been explored. |
Zhenliang Zhang; Junzhe Zhang; Xinyu Hu; Huixuan Zhang; Xiaojun Wan; |
| 266 | FairAD: Computationally Efficient Fair Graph Clustering Via Algebraic Distance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: It is, however, computationally challenging to incorporate fairness constraints into existing graph clustering algorithms, particularly for large graphs. To address this problem, we propose FairAD, a computationally efficient fair graph clustering method. |
Minh Phu Vuong; Young-Ju Lee; Iv\'{a}n Ojeda-Ruiz; Chul-Ho Lee; |
| 267 | PMTA: Perception-Aware Multi-Task Transformer Network for Personalized Multi-Domain Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods often struggle with efficient knowledge transfer across tasks and domains due to semantic gaps and distribution shifts. To address these challenges, we propose the Perception-Aware Multi-Task Transformer Network for Personalized Multi-Domain Adaptation (PMTA), a unified framework that integrates three key innovations: First, the Task Prompt Encoding (TPE) module dynamically generates prompts by synthesizing personalized user data with task-specific information. |
Chenbin Zhang; Xiaoxie Zhu; Xingchao Cao; Qiwei Chen; Feng Zhang; Yang Xiao; Zuotao Liu; |
| 268 | Variety Is The Spice of Life: Detecting Misinformation with Dynamic Environmental Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the static assumption is often violated, since in real-world scenarios, the veracity of news articles may vacillate within the dynamically evolving social environment. To tackle this problem, we propose a novel framework, namely Misinformation detection with Dynamic Environmental Representations (MISDER). |
Bing Wang; Ximing Li; Yiming Wang; Changchun Li; Jiaxu Cui; Renchu Guan; Bo Yang; |
| 269 | TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, this approach faces two primary challenges: (1) denoising raw external knowledge and (2) adapting semantic representations. To address these challenges, we propose exTernal knowledge-enhanced RecommendAtion With LLM assistance (TRAWL). |
Weiqing Luo; Chonggang Song; Lingling Yi; Gong Cheng; |
| 270 | Empirical Analysis on User Profile in Personalized LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Within the user profile, we reveal that it is the historical personalized response produced or approved by users that plays a pivotal role in personalizing LLMs. |
Bin Wu; Zhengyan Shi; Hossein A. Rahmani; Varsha Ramineni; Emine Yilmaz; |
| 271 | DocPolicyKG: A Lightweight LLM-Based Framework for Knowledge Graph Construction from Chinese Policy Documents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these, we propose DocPolicyKG, a novel framework for constructing knowledge graphs from Chinese policy documents using lightweight large language models (LLMs), integrating domain ontology, fine-tuning, and prompt engineering. |
Chen Han; Yuanyuan Li; Xijin Tang; |
| 272 | GSTBench: A Benchmark Study on The Transferability of Graph Self-Supervised Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing graph SSL methods are developed and evaluated under a single-dataset setting, leaving their cross-dataset transferability largely unexplored and limiting their ability to leverage knowledge transfer and large-scale pretraining, factors that are critical for developing generalized intelligence beyond fitting training data. To address this gap and advance foundation model research for graphs, we present GSTBench, the first systematic benchmark for evaluating the transferability of graph SSL methods. |
Yu Song; Zhigang Hua; Yan Xie; Jingzhe Liu; Bo Long; Hui Liu; |
| 273 | Federated Approximate Query Processing Based on Deep Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address the limitations above, we propose a secure federated approximate query system based on a deep classifier (SAQDC). |
Yutong Xie; Qingzhi Ma; Lei Zhao; An Liu; |
| 274 | Higher-Order Information Matters: A Representation Learning Approach for Social Bot Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these approaches overlook two key factors: the similarity of a user and her neighbors, as well as the coordinated behaviors of social bots, resulting in a suboptimal detection performance. To address these issues, we propose HyperScan, a novel representation learning method for social bot detection. |
Min Gao; Qiang Duan; Boen Liu; Yu Xiao; Xin Wang; Yang Chen; |
| 275 | FediData: A Comprehensive Multi-Modal Fediverse Dataset from Mastodon Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, their decentralized architecture presents two key challenges: 1) Distributed data and inconsistent access strategies across several individual instances make a unified collection difficult; 2) user-generated content (UGC) contains multiple modalities while lacking standard organization and high-quality annotation. To address these issues, we constructed FediData, a comprehensive multi-modal dataset from Mastodon. |
Min Gao; Haoran Du; Wen Wen; Qiang Duan; Xin Wang; Yang Chen; |
| 276 | Information Diffusion Prediction Based on User Multi-Dimensional Feature Interaction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces a model that leverages multi-dimensional interactions between user features. |
Jiaxing He; Yang Fang; Tianyang Shao; Xiang Zhao; |
| 277 | Contextual Representation Anchor Network for Mitigating Selection Bias in Few-Shot Drug Discovery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This bias in data representativeness leads to suboptimal performance. To overcome this challenge, we present a novel method named Contextual Representation Anchor Network (CRANet), where an anchor refers to a cluster center of the representations of molecules and serves as a bridge to transfer enriched contextual knowledge into molecular representations and enhance their expressiveness. |
Ruifeng Li; Wei Liu; Xiangxin Zhou; Mingqian Li; Qiang Zhang; Hongyang Chen; Xuemin Lin; |
| 278 | Mixture of Semantic and Spatial Experts for Explainable Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, the modality diversity of different traffic prediction scenarios (e.g., flow, speed, and demanding) remains to be underexplored, which restricts the model flexibility towards downstream applications. To mitigate these limitations, we propose a Mixture of Semantic and Spatial Experts (SS-MoE) for traffic prediction along with the human-intelligible post-hoc result explanation. |
Yang Hu; Shaobo Li; Dawen Xia; Zhiheng Zhou; Wenyong Zhang; Huaqing Li; Xingxing Zhang; Senzhang Wang; |
| 279 | Cequel: Cost-Effective Querying of Large Language Models for Text Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, leveraging LLMs at scale introduces substantial computational and financial costs due to the large number of required API queries or inference calls. To address this issue, we propose Cequel, a cost-effective framework that achieves accurate text clustering under a limited budget of LLM queries. |
Hongtao Wang; Taiyan Zhang; Renchi Yang; Jianliang Xu; |
| 280 | GCoder: Improving Large Language Model for Generalized Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current LLMs still face challenges related to closed-source restrictions, deployment difficulties, code quality, and generalization. To address these limitations, we propose GCoder, a code-based LLM specifically designed to enhance performance in generalized graph reasoning tasks. |
Qifan Zhang; Xiaobin Hong; Jianheng Tang; Nuo Chen; Yuhan Li; Wenzhong Li; Jing Tang; Jia Li; |
| 281 | SolarMAE: A Unified Framework for Regional Centralized and Distributed Solar Power Forecasting with Weather Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, the rapid growth and difficulties in real-time data collection associated with distributed solar systems exacerbate the complexity of regional gross solar power forecasting. To address these issues, we propose SolarMAE, a unified regional solar power forecasting framework enabling end-to-end precise forecasting for both centralized and distributed solar systems. |
Jin Wang; Bingqing Peng; Wenwei Wang; Yuanjie Hu; Yuejiang Chen; Peisong Niu; Liang Sun; |
| 282 | Energy-Guided Diffusion Sampling for Long-Term User Behavior Prediction in Reinforcement Learning-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While offline reinforcement learning methods leverage extensive datasets to address these issues, they often struggle with noisy data and fail to capture long-term user preferences, resulting in suboptimal recommendation policies. To overcome these limitations, we propose Diffusion-enhanced Actor-Critic for Offline RL4RS (DAC4Rec), a novel framework that integrates diffusion processes with reinforcement learning to model complex user preferences more effectively. |
Xiaocong Chen; Siyu Wang; Lina Yao; |
| 283 | Maximum In-Support Return Modeling for Dynamic Recommendation with Language Model Prior Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce MDT4Rec, an offline RLRS framework that builds on the Decision Transformer (DT) to address two major challenges: learning from sub-optimal histories and representing complex user-item interactions. |
Xiaocong Chen; Siyu Wang; Lina Yao; |
| 284 | Beyond Masking: Landmark-based Representation Learning and Knowledge-Distillation for Audio-Visual Deepfake Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Audio-visual deepfake detection methods demonstrate strong performance on academic datasets but fail significantly when applied to real-world. |
Chan Park; Muhammad Shahid Muneer; Simon S. Woo; |
| 285 | Incremental Learning for LLM-based Tokenization and Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these, we propose Reformer, an incremental learning framework to fine-tune RecLLMs and learnable tokenizers at each period. |
Haihan Shi; Xinyu Lin; Wenjie Wang; Wentao Shi; Junwei Pan; Jiang Jie; Fuli Feng; |
| 286 | UniECS: Unified Multimodal E-Commerce Search Framework with Gated Cross-modal Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current e-commerce multimodal retrieval systems face two key limitations: they optimize for specific tasks with fixed modality pairings, and lack comprehensive benchmarks for evaluating unified retrieval approaches. To address these challenges, we introduce UniECS, a unified multimodal e-commerce search framework that handles all retrieval scenarios across image, text, and their combinations. |
Zihan Liang; Yufei Ma; Zhipeng Qian; Huangyu Dai; Zihan Wang; Ben Chen; Chenyi Lei; Yuqing Ding; Han Li; |
| 287 | EAPformer: Entropy-Aware Patch Transformer for Multivariate Long-Term Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, they face limitations with static patching, which disrupts temporal continuity, fails to adapt to shifts between periodic and volatile patterns, and overlooks dynamic interactions between time segments and variables. To address these limitations, we propose Entropy-Aware Patch Transformer (EAPformer) which dynamically segments time series for differentiated assessments of historical patterns. |
Jiahao Ling; Xuan Yang; Shimin Gong; Bo Gu; |
| 288 | Augmenting Limited and Biased RCTs Through Pseudo-Sample Matching-Based Observational Data Fusion Method Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, existing data fusion methods are challenging to implement effectively in complex industrial settings due to the high dimensionality of features and the strict assumptions that are hard to verify with real-world data. To address these issues, we propose an empirical data fusion method called pseudo-sample matching. |
Kairong Han; Weidong Huang; Taiyang Zhou; Peng Zhen; Kun Kuang; |
| 289 | Spatial Semantic-based Enhanced Address Parsing Via Adaptive Weighted Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we focus on developing a robust framework to map diverse address descriptions into a unified semantic space of standardized addresses. |
Huiling Qin; Ming Wang; Yuanxun Li; Junbo Zhang; Yu Zheng; |
| 290 | LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: This paper introduces LatentExplainer, a framework for automatically generating semantically meaningful explanations of latent variables in deep generative models. |
Mengdan Zhu; Raasikh Kanjiani; Jiahui Lu; Andrew Choi; Qirui Ye; Liang Zhao; |
| 291 | Let Topology Speak: Graph Neural Network with Topology-Aware Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Similarly, Graph Transformers show strong performance but suffer from prohibitively high complexity. To overcome these challenges, we propose the Graph Neural Network with Topology-Aware Augmentation (GTA). |
Kangzhuo Chen; Xiaoqian Sun; Huawei Shen; Xueqi Cheng; |
| 292 | HyperGenFL: Hypernetwork-Generated Model Aggregation in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We suggest that one potential solution to this problem lies in weighting the model aggregation by client importance and client-to-client relationships. Based on this idea, we propose HyperGenFL (HG-FL), a hypernetwork that generates aggregation weights from learnable client embeddings without requiring any training or benchmarking data. |
Jerry Chen; Qikai Lu; Ruiqing Tian; Di Niu; Baochun Li; |
| 293 | Measuring Uncertainty in Medical Image Diagnosis Via Conformal Focal Loss Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Conformal Focal Loss (CFL), a principled approach that leverages the focal loss and the statistical validity of conformal prediction to better characterize diagnostic uncertainty. |
Ao Yang; Xiaodong Yue; Yufei Chen; |
| 294 | ActiViz: Understanding Sample Selection in Active Learning Through Boundary Visualization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: No research has effectively uncovered or explained the reasons behind these performance variations, leaving a gap in understanding of the factors that influence the success or failure of AL methods. To address this issue, we propose a novel method and tool leveraging Voronoi Diagrams to visualize AL processes by illustrating interactions between classification decision boundary changes and queried samples across AL iterations. |
Jie Chen; Honghui Du; Dairui Liu; Siteng Ma; Brian Mac Namee; Ruihai Dong; |
| 295 | X-Troll: EXplainable Detection of State-Sponsored Information Operations Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Large Language Models (LLMs) achieve strong performance on general natural language processing (NLP) tasks, they struggle with subtle propaganda detection and operate as ”black boxes”, providing no interpretable insights into manipulation strategies. This paper introduces X-Troll, a novel framework that bridges this gap by integrating explainable adapter-based LLMs with expert-derived linguistic knowledge to detect state-sponsored trolls and provide human-readable explanations for its decisions. |
Lin Tian; Xiuzhen Zhang; Maria Myung-Hee Kim; Jennifer Biggs; Marian-Andrei Rizoiu; |
| 296 | DiRW: Path-Aware Digraph Learning for Heterophily Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Despite recent advancements, existing spatial- and spectral-based DiGNNs have limitations due to their complex learning mechanisms and reliance on high-quality topology, resulting in low efficiency and unstable performance. To address these issues, we propose Directed Random Walk (DiRW), a plug-and-play strategy for most spatial-based DiGNNs and also an innovative model which offers a new digraph learning paradigm. |
Daohan Su; Xunkai Li; Zhenjun Li; Yinping Liao; Rong-Hua Li; Guoren Wang; |
| 297 | A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, our analysis reveals that (1) existing multi-behavior recommender systems exhibit a significant gap in recommendation quality between the two item types (visited and unvisited items) and (2) achieving strong performance on both types with a single model architecture remains hallenging. To tackle these issues, we propose a novel multi-behavior recommender system, MEMBER. |
Kyungho Kim; Sunwoo Kim; Geon Lee; Kijung Shin; |
| 298 | From Policy Comparison to Process Consistency and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the SPC framework implicitly assumes the invariance of decision environments, and therefore fails to address a flurry of real-world data science applications. In this work, we refer to this overlooked issue as environment consistency, and together with policy consistency, this extends to a generalized concept process consistency for systematically comparing policy trials under the Markov decision process (MDP) framework. |
Yifan Xu; Yujia Yin; Yiming Xing; Yifan Chen; |
| 299 | M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These limitations pose significant challenges for the efficient deployment and operation of deep learning models on large-scale datasets. To address these challenges, we propose a cost-effective graph-free Multilayer Perceptron (MLP) based model M3-Net for traffic prediction. |
Guangyin Jin; Sicong Lai; Xiaoshuai Hao; Jinlei Zhang; Mingtao Zhang; |
| 300 | Stamp: Semantic-Aware Sub-trajectory Anomaly Detection with Diffusion Multi-model Pool for Evolving Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such limitations make it impossible to detect abnormal trajectories in a timely and semantically comprehensive manner. To fill this gap, we propose Stamp, a novel framework for Semantic-aware sub-Trajectory Anomaly detection with a diffusion Multi-model Pool. |
Biao Chen; Junhua Fang; Pingfu Chao; An Liu; Pengpeng Zhao; Lei Zhao; |
| 301 | Non-autoregressive Generative Auction with Global Externalities for Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose the Non-autoregressive Generative Auction with global externalities (NGA), a novel end-to-end auction framework for industrial online advertising. |
Zuowu Zheng; Ze Wang; Fan Yang; Wenqing Ye; Weihua Huang; Wenqiang He; Teng Zhang; Xingxing Wang; |
| 302 | KALE: Knowledge Aggregation for Label-free Model Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This dependency makes fine-tuning impractical for niche applications, such as rare object detection or specialized medical tasks. To overcome these limitations, we propose KALE: Knowledge Aggregation for Label-free model Enhancement, a label-free method for model enhancement, leveraging knowledge aggregation via model fusion and adaptive representation alignment. |
Yuebin Xu; Xuemei Peng; Zhiyi Chen; Zeyi Wen; |
| 303 | TCPN: Temporal Pyramidal Recurrent Network with Contrastive Learning for Temporal Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose TCPN, a novel Temporal Pyramidal Recurrent Network with contrastive learning for TKG extrapolation reasoning. |
Liu Yang; Zixuan Luo; Tingxuan Chen; Zidong Wang; Limin Liu; |
| 304 | On The Cross-type Homophily of Heterogeneous Graphs: Understanding and Unleashing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This structural diversity complicates the analysis of homophily, as traditional homophily metrics fail to account for distinct label spaces across node types. To address this limitation, we introduce the Cross-Type Homophily Ratio (CHR), a novel metric that quantifies homophily based on the similarity of target information across different node types. |
Zhen Tao; Ziyue Qiao; Chaoqi Chen; Zhengyi Yang; Lun Du; Qingqiang Sun; |
| 305 | MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, cross-slice inconsistencies and data quality variability present significant analytical challenges. To overcome these limitations, we developed MUSE, a computational framework designed for multi-slice joint embedding, spatial domain identification, and gene expression imputation. |
Ziheng Duan; Xi Li; Zhiqing Xiao; Rex Ying; Jing Zhang; |
| 306 | Asking Questions with Thoughts: An Efficient Difficulty-Controllable Question Generation Method with Posterior Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Difficulty Controllable Question Generation (DCQG) for reading comprehension learns to generate questions for measuring the reading abilities of examinees, playing a crucial role … |
Sixing Wu; Jiahao Chen; Yujue Zhou; Zhijun Yang; Wei Zhou; |
| 307 | NeighSqueeze: Compact Neighborhood Grouping for Efficient Billion-Scale Heterogeneous Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, they introduce substantial information loss, particularly affecting high-degree nodes and decreasing predictive accuracy. To address this, we introduce NeighSqueeze, a novel approach that groups structurally and semantically similar nodes, substantially reducing the neighbors count and facilitating full-neighbor learning. |
Xinyue Feng; Shuxin Zhong; Jinquan Hang; Yuequn Zhang; Guang Yang; Haotian Wang; Desheng Zhang; Guang Wang; |
| 308 | USE-LTV: Customer LifeTime Value Prediction Via Uncertain Sequence Modeling in Baidu Ads Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing solutions for LTV prediction usually rely on determined historical sequence data, which are challenging to apply in Baidu ads due to two unique features including (i) uncertain behavior sequence of customers caused by dynamic advertising strategies, (ii) complex and long-tail distribution of LTV caused by continuous customer behaviors and unique business pattern of search and news feed ads in Baidu. To incorporate these new factors, we propose an Uncertain behavior Sequence modeling framework to predict customer LifeTime Value (USE-LTV), where we (i) utilize a transformer module to extract uncertain sequence features, and develop a dynamic weight mechanism to capture differentiated information under uncertain behavior sequence, (ii) design an continuous loss function tailored to the real-world long-tail exponential LTV distribution in Baidu ads. |
Lei Yang; Jiahui Zhang; Guoyu Liu; Houzhi Wang; Zhiyuan Zhou; Xiaohui Zhao; |
| 309 | Advanced Privacy Protection in Federated Learning Using Server-initiated Homomorphic Encryption Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a new privacy protection scheme for FL that uses homomorphic encryption (HE), noise, and secret sharing to protect users’ sensitive data from up to n-2 adversarial clients and the server colluding. |
Cameron Lee; Matthew L. Daggitt; Yansong Gao; Jin B. Hong; |
| 310 | M-LLM3REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing solutions either generate pseudo-interaction sequence, which often introduces redundant or noisy signals, or rely heavily on semantic similarity, overlooking dynamic shifts in user motivation. To address these limitations, this paper proposes a novel recommendation framework, termed M-LLM3REC, which leverages large language models for deep motivational signal extraction from limited user interactions. |
Lining Chen; Qingwen Zeng; Huaming Chen; |
| 311 | Generative Data Augmentation in Graph Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel framework, GDA4Rec (Generative Data Augmentation in graph contrastive learning for Recommendation) to generate high-quality augmented views and provide robust self-supervised signals. |
Yansong Wang; Qihui Lin; Junjie Huang; Tao Jia; |
| 312 | WDformer: A Wavelet-based Differential Transformer Model for Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We proposed WDformer, a wavelet-based differential Transformer model. |
Xiaojian Wang; Chaoli Zhang; Zhonglong Zheng; Yunliang Jiang; |
| 313 | GegenNet: Spectral Convolutional Neural Networks for Link Sign Prediction in Signed Bipartite Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Our extensive empirical studies reveal that GegenNet can achieve significantly superior performance (up to a gain of 4.28\% in AUC and 11.69\% in F1) in link sign prediction compared to 11 strong competitors over 6 benchmark SBG datasets. |
Hewen Wang; Renchi Yang; Xiaokui Xiao; |
| 314 | The Structure of Cross-National Collaboration in Open-Source Software Development Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Using the GitHub Innovation Graph dataset, which aggregates the complete cross-country collaborations among the entire population of GitHub developers, we present quantitative evidence of deep-seated religious and cultural affinities, shared colonial histories, and geopolitical factors structuring the collaborations between non-U.S. country pairs that become visible when the overarching dominance of the U.S. is removed from the data. |
Henry Xu; Katy Yu; Hao He; Hongbo Fang; Bogdan Vasilescu; Patrick S. Park; |
| 315 | Neighbor-enhanced Graph Pre-training and Prompt Learning Framework for Fraud Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Nevertheless, the practical application of graph prompt learning in real-world fraud detection is still constrained, as they may exhibit bias when dealing with multiplex transaction networks and may fail to model the intrinsic relationships between nodes and their neighbors, which is crucial for effective fraud detection. To address these two challenges, we propose GPCF, an efficient graph pre-training and prompt learning framework. |
Ziyang Cheng; Jie Yang; Yixin Song; Dawei Cheng; Guang Yang; Bo Wang; |
| 316 | Fast Outlier Detection in Oblique Subspaces Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce a new notion of oblique subspaces defined on pairwise object proximity functions without requiring explicit multidimensional representations of the underlying data. |
Bowen Li; Charu C. Aggarwal; Peixiang Zhao; |
| 317 | Revisiting Long-Tailed Learning: Insights from An Architectural Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose LT-DARTS, a NAS method with a novel search space and search strategy specifically designed for LT data. |
Yuhan Pan; Yanan Sun; Wei Gong; |
| 318 | Improving Content Anomaly Detection on Social Media Via Counterfactual Mitigation of Social Event-Induced Bias Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the prevalence of major real-world social events can introduce model bias that skews, narrows, and undermines a detector’s ability to perceive anomaly in content by contaminating and homogenizing the expressions posted during these events. To address this challenge, we propose SeiNS, a novel, model-agnostic plugin designed to mitigate the bias induced by prevalent social events. |
Jiaxin Li; Geng Zhao; |
| 319 | Hypercomplex Prompt-aware Multimodal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing methods suffer from three fundamental limitations: (1) restricted ability to represent rich multimodal features through a single representation, (2) existing linear modality fusion strategies ignore the deep nonlinear correlations between modalities, and (3) static optimization methods failing to dynamically mitigate the over-smoothing problem in graph convolutional network (GCN). To overcome these limitations, we propose HPMRec, a novel Hypercomplex Prompt-aware Multimodal Recommendation framework, which utilizes hypercomplex embeddings in the form of multi-components to enhance the representation diversity of multimodal features. |
Zheyu Chen; Jinfeng Xu; Hewei Wang; Shuo Yang; Zitong Wan; Haibo Hu; |
| 320 | When Words Can’t Capture It All: Towards Video-Based User Complaint Text Generation with Multimodal Video Complaint Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Additionally, we present a new complaint retention (CR) evaluation metric that discriminates proposed (CoD-V) task against standard video summary generation and description task. To strengthen this initiative, we introduce a multimodal Retrieval-Augmented Generation (RAG) embedded VideoLLaMA2-7b model, designed to generate complaints while accounting for the user’s emotional state. |
Sarmistha Das; R E Zera Marveen Lyngkhoi; Kirtan Jain; Vinayak Goyal; Sriparna Saha; Manish Gupta; |
| 321 | Caption, Create, Continue: Continual Learning with Pre-trained Generative Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose CLTS (Continual Learning via Text-Image Synergy), a novel class-incremental framework that mitigates forgetting without storing real task data. |
Indu Solomon; Aye Phyu Phyu Aung; Uttam Kumar; Senthilnath Jayavelu; |
| 322 | Anomaly Detection for Advanced Driver Assistance System with NCDE-based Normalizing Flow Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Graph Neural Controlled Differential Equation Normalizing Flow (GDFlow), which leverages Normalizing Flow (NF) with Neural Controlled Differential Equations (NCDE) to learn the distribution of normal driving patterns. |
Kangjun Lee; Minha Kim; Youngho Jun; Simon S. Woo; |
| 323 | RottenReviews: Benchmarking Review Quality with Human and LLM-Based Judgments Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce RottenReviews, a benchmark designed to facilitate systematic assessment of review quality. |
Sajad Ebrahimi; Soroush Sadeghian; Ali Ghorbanpour; Negar Arabzadeh; Sara Salamat; Muhan Li; Hai Son Le; Mahdi Bashari; Ebrahim Bagheri; |
| 324 | Temporal Blocks with Memory Replay for Dynamic Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address this issue, we construct temporal blocks with the memory replay mechanism by sequentially merging several adjacent snapshots to capture long-range temporal patterns and causal dependencies over time. Building on this, we propose a novel dynamic graph representation learning model named TBD. |
Zhigang Yu; Hao Yan; Ruochen Liu; Xianghan Wang; Haijun Zhang; Senzhang Wang; |
| 325 | SG-Filter: Enhancing Similar Text Retrieval Via Hierarchical Summarized-Semantic Index and Adaptive Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods using summarized info still have a critical challenge. Their vectorization-based approaches fail to effectively model the global relationship in the summarized information, resulting in a further 79\% deterioration in recall rate.To address this challenges, we present the SG-Filter, a novel retrieval framework that integrates summarized information by designing the hierarchical summarized-semantic index and the adaptive filtering strategy applied on it. |
Jiancai Ye; Jun Liu; Haoyu Zhang; Maojia Sheng; Tao Yang; Jiaming Xu; Jinhao Li; Yu Wang; Guohao Dai; |
| 326 | BrainX: A Universal Brain Decoding Framework with Feature Disentanglement and Neuro-Geometric Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Current approaches often rely on partially shared model architectures that offer limited generalization and still require subject-specific components, restricting their applicability to unseen subjects. To address this limitation, we propose BrainX, a universal brain decoding framework that constructs a unified fMRI encoder and image generator to achieve subject-agnostic modeling. |
Zheng Cui; Dong Nie; Pengcheng Xue; Xia Wu; Daoqiang Zhang; Xuyun Wen; |
| 327 | Harnessing Light for Cold-Start Recommendations: Leveraging Epistemic Uncertainty to Enhance Performance in User-Item Interactions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, many models do not assess the efficiency with which they utilize the available training knowledge, leading to the extraction of significant knowledge that is not fully used, thus limiting improvements in cold-start performance. To address this, we introduce the concept of epistemic uncertainty (which refers to uncertainty caused by a lack of knowledge of the best model) to indirectly define how efficiently a model uses the training knowledge. |
Yang Xiang; Li Fan; Chenke Yin; Menglin Kong; Chengtao Ji; |
| 328 | See Beyond A Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Abstract: Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user … |
Sishuo Chen; Zhangming Chan; Xiang-Rong Sheng; Lei Zhang; Sheng Chen; Chenghuan Hou; Han Zhu; Jian Xu; Bo Zheng; |
| 329 | Addressing The Distortion of Community Representations in Anomaly Detection on Attributed Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: And the key oversight is to treat all community members equally and ignore the relative reliability of nodes. To address these issue, we propose a CL-based ANomaly detectIon Method on Attributed networks targeted at mitigating community distortions to enhance anomaly discrimination (ANIMA for short), which incorporates a Truncation-Restriction community encoder (TRC-Encoder) with an elaborate heuristic prior instruction to detect and suppress anomalous contributions during community representation learning. |
Enbo He; Yitong Hao; Yue Zhang; Guisheng Yin; Lina Yao; |
| 330 | STORM: Spatio-Temporal Similar Trajectory Retrieval on Non-Uniform Maritime Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing algorithms struggle to effectively model the irregularity of non-uniformly sampled maritime trajectories, leading to reduced performance in similar trajectory retrieval. In this demonstration, we present STORM, a system designed to effectively retrieve the top-k similar trajectories, which supports both user-specified and automated query settings. |
Xiaolin Han; Yonghao Zhou; Chenhao Ma; Fang Li; Xuequn Shang; |
| 331 | Enabling Group Fairness in Machine Unlearning Via Distribution Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: From a preliminary study, we found that a model can become more biased after applying unlearning algorithms. To address this issue, we propose FMU (Fair Machine Unlearning), which ensures group fairness throughout the unlearning process. |
Yezi Liu; Yanning Shen; |
| 332 | GCLS2: Towards Efficient Community Detection Using Graph Contrastive Learning with Structure Semantics Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structure semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS2) to detect communities. |
Qi Wen; Yiyang Zhang; Yutong Ye; Yingbo Zhou; Nan Zhang; Xiang Lian; Mingsong Chen; |
| 333 | Collaborative Interest Mining Network for Knowledge Graph-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a novel recommendation model called Collaborative Interest Mining Network for Knowledge Graph-based Recommendation (CIMNK), which leverages the knowledge graph to mine collaborative interest similarity (i.e., the similarity between users who share the same interests), thereby enhancing the quality of user embeddings. |
Jie Luo; Ying Pan; Guoliang Huang; |
| 334 | Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present SolarCAST, a causally informed model predicting future global horizontal irradiance (GHI) at a target site using only historical GHI from site X and nearby stations S—unlike prior work that relies on sky-camera or satellite imagery requiring specialized hardware and heavy preprocessing. |
Yanan Niu; Demetri Psaltis; Christophe Moser; Luisa Lambertini; |
| 335 | General Adaptive Memory Allocation for Learned Bloom Filters Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose Gama, the first General Adaptive Memory Allocation framework for LBFs as far as we know. |
You Shang; Xiang He; Ruiyuan Li; Yingying Sun; Guanyao Li; Guangchao Yang; Junbo Zhang; Yu Zheng; |
| 336 | Multi-Ontology Integration with Dual-Axis Propagation for Medical Concept Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose LINKO, a large language model (LLM)-augmented integrative ontology learning framework that leverages multiple ontology graphs simultaneously by enabling dual-axis knowledge propagation both within and across heterogeneous ontology systems to enhance medical concept representation learning. |
Mohsen Nayebi Kerdabadi; Arya Hadizadeh Moghaddam; Dongjie Wang; Zijun Yao; |
| 337 | MGSTDN: Multi-Granularity Spatial-Temporal Diffusion Network for Next POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods often fall short in capturing the comprehensive multi-granularity spatial-temporal correlations due to three primary limitations: (1) users’ complex mobility patterns entangled in single trajectory data, (2) limited mobility patterns details due to independent modeling at each granularity, and (3) low inference efficiency in cascaded multi-granularity predictions. To tackle these challenges, we propose a novel approach that models transformations across different granularities in both spatial regions and temporal periods as a diffusion process, leading to the development of the Multi-Granularity Spatial-Temporal Diffusion Network (MGSTDN). |
Zhuang Zhuang; Haitao Yuan; Shanshan Feng; Heng Qi; Yanming Shen; Baocai Yin; |
| 338 | FUTURE: Flexible Unlearning for Tree Ensemble Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods are often tailored to a particular model or rely on the discrete tree structure, making them difficult to generalize to complex ensembles and inefficient for large-scale datasets. To address these limitations, we propose FUTURE, a novel unlearning algorithm for tree ensembles. |
Ziheng Chen; Jin Huang; Jiali Cheng; Yuchan Guo; Mengjie Wang; Lalitesh Morishetti; Kaushiki Nag; Hadi Amiri; |
| 339 | Contrastive Multi-View Graph Hashing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose Contrastive Multi-view Graph Hashing (CMGHash), a novel end-to-end framework designed to learn unified and discriminative binary embeddings from multi-view graph data. |
Yang Xu; Zuliang Yang; Kai Ming Ting; |
| 340 | Structural Entropy-based Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing graph construction methods often produce structures that fail to preserve key temporal and cross-variable dependencies, introducing redundant or irrelevant connections. To address these challenges, we propose a structural entropy-based approach for MTS forecasting. |
Xinhui Li; Kun Yue; Lixing Yu; Peizhong Yang; |
| 341 | Anchor-based Pairwise Comparison Via Large Language Model for Recommendation Reranking Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address their limitations, we propose the APCR, an Anchor-based Pairwise Comparison for recommendation Reranking in this paper. |
Qin Luo; Erjia Chen; Zhao Shi; Bang Wang; |
| 342 | Modeling Edge-Specific Node Features Through Co-Representation Neural Hypergraph Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Additionally, WHATsNet suffers from the common oversmoothing issue in most HGNNs. To address these limitations, we propose CoNHD, a novel HGNN architecture specifically designed to model edge-specific features for ENC. |
Yijia Zheng; Marcel Worring; |
| 343 | AdaHet-MKD: An Adaptive Heterogeneous Multi-teacher Knowledge Distillation for Medical Image Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Secondly, they overlook the complementary information in the CLIP model where the text encoder and image encoder can be leveraged as heterogeneous information to teach one single modality. To tackle these challenges, we propose an Adaptive Heterogeneous Multi-teacher Knowledge Distillation (AdaHet-MKD) framework for effective knowledge transfer across heterogeneous text-image models and among multiple teacher models. |
Helin Wang; Wei Du; Ning Liu; Qian Li; Yanyu Xu; Lizhen Cui; |
| 344 | Relational Multi-Path Enhancement for Extrapolative Relation Reasoning in Temporal Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional approaches face inherent limitations in capturing complex semantic correlations and structural patterns among relations. To tackle this problem, we propose the Relational Multi-path Enhancement network (RME), which primarily focuses on relation modeling to enrich relation representations through comprehensive multi-path analysis. |
Linlin Zong; Chi Ma; Jiahui Zhou; Xinyue Liu; Wenxin Liang; Xianchao Zhang; Bo Xu; |
| 345 | Structuring Video Semantics with Temporal Triplets for Zero-Shot Video Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a structured representation based on temporal triplets to address two major challenges in traditional approaches: temporal fragmentation and entity reference ambiguity. |
Linlin Zong; Xinyu Zhai; Xinyue Liu; Wenxin Liang; Xianchao Zhang; Bo Xu; |
| 346 | VideoAVE: A Multi-Attribute Video-to-Text Attribute Value Extraction Dataset and Benchmark Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing AVE datasets are primarily limited to text-to-text or image-to-text settings, lacking support for product videos, diverse attribute coverage, and public availability. To address these gaps, we introduce VideoAVE, the first publicly available video-to-text e-commerce AVE dataset across 14 different domains and covering 172 unique attributes. |
Ming Cheng; Tong Wu; Jiazhen Hu; Jiaying Gong; Hoda Eldardiry; |
| 347 | UXSim: Towards A Hybrid User Search Simulation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: https://searchsim.org/uxsim, a novel framework that integrates both approaches. |
Saber Zerhoudi; Michael Granitzer; |
| 348 | A Privacy-preserving Spatial Dataset Joinable Search in Cloud Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To further enhance efficiency and reduce storage cost, we propose an optimized scheme (PDJDS+), which constructs a coarse-grained grid-based inverted index to filter candidate datasets and integrates a joinable coverage distinction check table to expedite the evaluation of spatial dataset coverage distinction. |
Zhengkai Zhang; Hua Dai; Hao Zhou; Mingfeng Jiang; Pengyue Li; Geng Yang; |
| 349 | ParaStyleTTS: Toward Efficient and Robust Paralinguistic Style Control for Expressive Text-to-Speech Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose ParaStyleTTS, a lightweight and interpretable TTS framework that enables expressive style control from text prompts alone. |
Haowei Lou; Hye-young Paik; Wen Hu; Lina Yao; |
| 350 | Achoio: A Skill-Aware Evaluation Management System for Text-To-Speech Research Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce Achoio, a dedicated end-to-end online system designed to streamline and scale human evaluation for the TTS research community. |
Haowei Lou; Hye-Young Paik; Basem Suleiman; Wen Hu; Lina Yao; |
| 351 | An Interventional Approach to Real-Time Disaster Assessment Via Causal Attribution Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we provide an alternative interventional tool that complements traditional disaster modelling tools by leveraging real-time data sources like satellite imagery, news, and social media. |
Saketh Vishnubhatla; Alimohammad Beigi; Rui Heng Foo; Umang Goel; Ujun Jeong; Bohan Jiang; Adrienne Raglin; Huan Liu; |
| 352 | Reinforcement Learning-Driven Generative Retrieval with Semantic-aligned Multi-Layer Identifiers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current methods often struggle with two major challenges: limited identifier quality and insufficient query-document interaction, leading to limited retrieval performance. To tackle these challenges, we propose a novel generative retrieval framework integrated with semantic-aligned multi-layer identifiers and reinforcement learning. |
Bo Xu; Yicen Tian; Xiaokun Zhang; Erchen Yu; Dailin Li; Linlin Zong; Hongfei Lin; |
| 353 | Tide: A Time-Wise Causal Debiasing Framework for Generative Dynamic Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, such time related features are intrinsically coupled, which makes simultaneously and independently modeling both features infeasible. Motivated by these issues, we propose a Time-wise causal debiasing framework (Tide) for generative dynamic link prediction, which does not resort to any extra trainable modules. |
Xin Zhang; Jianming Zheng; Fei Cai; Zhiqiang Pan; Wanyu Chen; Chonghao Chen; Honghui Chen; |
| 354 | KV-Auditor: Auditing Local Differential Privacy for Correlated Key-Value Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For non-interactive mechanisms, we propose horizontal KV-Auditor for small domains with sufficient samples and vertical KV-Auditor for large domains with limited samples. |
Jingnan Xu; Leixia Wang; Xiaofeng Meng; |
| 355 | Generative Recommendation with Semantic IDs: A Practitioner’s Handbook Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, the absence of an open-source, unified framework hinders systematic benchmarking and extension, slowing model iteration. To address this challenge, our work introduces and open-sources a framework for Generative Recommendation with semantic ID, namely GRID, specifically designed for modularity to facilitate easy component swapping and accelerate idea iteration. |
Clark Mingxuan Ju; Liam Collins; Leonardo Neves; Bhuvesh Kumar; Louis Yufeng Wang; Tong Zhao; Neil Shah; |
| 356 | Hierarchy-Consistent Learning and Adaptive Loss Balancing for Hierarchical Multi-Label Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Hierarchical Multi-Label Classification (HMC) faces critical challenges in maintaining structural consistency and balancing loss weighting in Multi-Task Learning (MTL). In order to address these issues, we propose a classifier called HCAL based on MTL integrated with prototype contrastive learning and adaptive task-weighting mechanisms. |
Ruobing Jiang; Mengzhe Liu; Haobing Liu; Yanwei Yu; |
| 357 | Flexiffusion: Training-Free Segment-Wise Neural Architecture Search for Efficient Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While Neural Architecture Search (NAS) can optimize DMs, existing methods are hindered by retraining requirements, exponential search complexity from step-wise optimization, and slow evaluation relying on massive image generation. To address these challenges, we propose Flexiffusion, a training-free NAS framework that jointly optimizes generation schedules and model architectures without modifying pre-trained parameters. |
Hongtao Huang; Xiaojun Chang; Lina Yao; |
| 358 | PersonaGen: A Persona-Driven Open-Ended Machine-Generated Text Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present PersonaGen, a novel dataset for investigating persona-driven machine-generated text (MGT) produced by Open Large Language Models (OLLMS). |
Carmelo Gugliotta; Lucio La Cava; Andrea Tagarelli; |
| 359 | Hyperbolic Prompt Learning for Incremental Event Detection with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose HPLLM, a hyperbolic prompt-enhanced large language model framework, motivated by the observation that both embedding distributions and dependency graphs in event datasets exhibit hyperbolic properties. |
Xiujin Zhang; Wenxin Jin; Haotian Hong; Pengfei Zhang; Jiting Li; Kongjing Gu; Hao Peng; Li Sun; |
| 360 | BiListing: Modality Alignment for Listings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes BiListing, for Bimodal Listing, an approach to align text and photos of a listing by leveraging large-language models and pretrained language-image models. |
Guillaume Guy; Mihajlo Grbovic; Chun How Tan; Han Zhao; |
| 361 | Internet of Things Dataset for Human Operator Activity Recognition in Industrial Environment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present a novel dataset for classifying human operator activities in a meat processing plant where production line operators use knives to cut, process and produce meat products. |
Abdur Forkan; Prem Prakash Jayaraman; Clarence Antonmeryl; Federico Montori; Abhik Banerjee; Kaneez Fizza; Dimitrios Georgakopoulos; |
| 362 | ORCAS: Obfuscation-Resilient Binary Code Similarity Analysis Using Dominance Enhanced Semantic Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, when code obfuscation is applied, the unstable control flow structure degrades their performance. To address this issue, we develop ORCAS, an Obfuscation-Resilient BCSA model based on Dominance Enhanced Semantic Graph (DESG). |
Yufeng Wang; Yuhong Feng; Yixuan Cao; Haoran Li; Haiyue Feng; Yifeng Wang; |
| 363 | Yes Is Harder Than No: A Behavioral Study of Framing Effects in Large Language Models Across Downstream Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To fill in the gap, in this paper, we conduct a systematic empirical investigation into framing effects in LLMs across multiple real-world downstream tasks. |
Ziheng Zhang; Weixin Zeng; Jiuyang Tang; Ji Wang; Xiang Zhao; |
| 364 | Beyond Return Conditioning: Multi-Scale Sequence Modeling and Advantage-Guided Policy Routing for Offline RL Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Moreover, its return-conditioning mechanism offers limited guidance in exploiting high-quality behavioral patterns, often resulting in suboptimal action generation during inference. To address these challenges, we propose the Advantage Decision ConvMamba (ADCM), a method that integrates multi-scale sequence modeling (MSSM) with advantage policy guidance (APG). |
Kunbao Wu; Xinning Zhu; Yang Qin; Tieru Wang; Jianzhou Diao; Zheng Hu; |
| 365 | Waypoint POI Recommendation for Vehicle Navigation Services Using Hierarchical Graphs and Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This scenario (e.g., suggesting a lunch stop during a road trip) differs from the conventional ”next POI” recommendation in that it infers waypoint POIs from only two (origin and destination) inputs and predicts multiple intermediate stops rather than a single next location. To solve this problem, we propose WayPOI, a novel recommender model for Waypoint POI suggestion based on hierarchical graph based contrastive learning (WayPOI). |
Jongsoo Lee; Heejun Shin; Namhyuk Kim; Dong-Kyu Chae; |
| 366 | From Rules to Flexibility: A Resource and Method for SEC Item Extraction in Post-2021 10-K Filings Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we propose a novel layout-robust segmentation approach that achieves identification of financial report by combining fuzzy matching and structural heuristics. |
Xiao Li; Changhong Jin; Ruihai Dong; |
| 367 | Geometric Heterogeneous Graph Neural Network for Protein-Ligand Binding Affinity Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we propose a novel Geometric Heterogeneous Graph Neural Network (GeoHGN) for PLA prediction. |
Feng Huang; Yuhang Xia; Ziyan Wang; Liuqing Yang; Wen Zhang; |
| 368 | PKGRec: Personal Knowledge Graph Construction and Mining for Federated Recommendation Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, these studies often overlook challenges associated with distributed PKGs across different users, such as joint training and privacy protection. To address these challenges, we propose PKGRec, a federated graph recommendation method specifically designed for PKGs, which utilizes a federated learning framework to ensure user privacy and data security during joint learning. |
Haochen Yuan; Yang Zhang; Quan Z. Sheng; Lina Yao; Yipeng Zhou; Xiang He; Zhongjie Wang; |
| 369 | OFedED: One-shot Federated Learning with Model Ensemble and Dataset Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Without any chance to iteratively correct these biases or mitigate heterogeneity, the aggregated model significantly deviates from the optimum achieved under dataset centralized training. To address this challenge, we propose OFedED, a one-shot FL framework that preserves privacy and fully exploits client data by combining local data distillation with server-side ensemble learning. |
Xuhui Li; Zhengquan Luo; Zihui Cui; Xin Cao; Zhiqiang Xu; |
| 370 | MSOFormer: Multi-scale Transformer with Orthogonal Embedding and Frequency Modeling for Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although Transformer-based models have recently achieved impressive results in this field, their performance is still hindered by three core challenges: complex temporal dependencies, diverse inter-variable correlations, and patterns that span multiple time scales. To address these issues, we propose MSOFormer-a Multi-scale Transformer with Orthogonal Embedding and Frequency Modeling. |
Qin Shi; Chu Xu; Zongtang Hu; Dong Shen; Dapeng Sun; Lijun Quan; |
| 371 | Give Me Some SALT: Structure-Aware Link Modeling for Temporal Weighted Link Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce SALT, a Structure-Aware Link modeling for Temporal weighted link prediction, which consists of Weighted Link Encoder (WLE) and Temporal Link State Space Module (TLSSM). |
Ting Li; Hanchen Wang; Yiran Li; Xiaolei Liu; |
| 372 | A Robust and High-Efficiency Active Clustering Framework with Multi-User Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a robust and high-efficiency Active Clustering framework with Multi-user Collaboration (ACMC). |
Wen-Bo Xie; Tian Zou; Tao Deng; Xuan-Lin Zhu; Xun Fu; Qiu-Yu Wang; Bin Chen; Xin Wang; |
| 373 | EI-KGC: A Knowledge Graph Completion Model Based on Fine-Grained Element Interactions Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address this issue, we define a three-level classification of element interactions: Interactions between Elements at the Head Entity (IEH), Interactions between Elements at the Relationship (IER), and Interactions between Elements at the Tail Entity (IET), that systematically models the influence propagation patterns among knowledge graph triples at element level. Based on these interaction types, we propose a novel Knowledge Graph Completion Model Based on Fine-Grained Element Interactions (EI-KGC). |
Dong Li; Lingling Zhang; Yuhang Fan; Jingyou Sun; Xinyu Zhang; Baoyan Song; |
| 374 | Querier-Aware LLM: Generating Personalized Responses to The Same Query from Different Queriers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we propose a new form of querier-aware LLM personalization, generating different responses even for the same query from different queriers. |
Hang Zeng; Chaoyue Niu; Fan Wu; Chengfei Lv; Guihai Chen; |
| 375 | GraFS: An Integrated GNN-LLM Approach for Inferring Best Functional Substitute Products Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce GraFS (Graph-enabled Large Language Model framework for Functional Substitute Selection), which combines Large Language Models (LLMs) to extract textual, semantic similarities from product descriptions and Graph Neural Networks (GNNs) that learn substitution patterns from customer behavior. |
Favour Nerrise; Edward W Huang; Xiaonan Ji; Karthik Subbian; Danai Koutra; |
| 376 | Towards Explainable Transaction Risk Analysis With Dual Graph Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, LLMs face: (1) insufficient adaptation to transaction data analysis, and (2) ineffective knowledge retrieval methods that ignore the rich graph structure of transaction data. To address these issues, we propose the Dual Graph Retrieval-Augmented Generation (Dual-gRAG) framework, which utilizes dual retrieval: expert knowledge and reasoning case retrieval. |
Liang Su; Mingyang Zhang; Kangxiang Jia; Tengfei Liu; Weiqiang Wang; Yun Xiong; Xixi Wu; Xinyu Gao; Yongrui Fu; Jiawei Zhang; |
| 377 | TwinBandit Prompt Optimizer: Adaptive Prompt Optimization Via Synergistic Dual MAB-Guided Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A common deficiency in Automatic Prompt Engineering (APE) is the failure to strategically employ specific failure feedback in concert with the adaptive and coordinated selection of diverse generation strategies. To address this deficiency, we introduce TwinBandit Prompt Optimizer (TBPO), an APE framework that employs a synergistic dual Multi-Armed Bandit (MAB) mechanism for adaptive prompt generation, applicable to black-box Large Language Models (LLMs). |
Young-Joon Park; Seong-Ryeong Lee; Anh-Dung Vo; Minsung Jung; Daewoo Choi; |
| 378 | Seeing Through The Blur: Unlocking Defocus Maps for Deepfake Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As such content becomes increasingly difficult to distinguish from reality, the integrity of visual media is under threat. To address this issue, we propose a physically interpretable deepfake detection framework and demonstrate that defocus blur can serve as an effective forensic signal. |
Minsun Jeon; Simon S. Woo; |
| 379 | Rethinking Client-oriented Federated Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose FedC4, which combines graph Condensation with C-C Collaboration optimization. |
Zekai Chen; Xunkai Li; Yinlin Zhu; Rong-Hua Li; Guoren Wang; |
| 380 | Adaptive Bidirectional State Space Model for High-frequency Portfolio Management Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, representing financial data is challenging for SSMs due to: 1) the non-stationary nature of financial markets and 2) the requirement of asset correlations for financial understanding. In this paper, under a deep reinforcement learning (DRL) paradigm for high-frequency portfolio management, we propose a novel Adaptive Bidirectional State Space Model (ABSSM) to tackle the above challenges. |
Wei Ding; Hanpeng Jiang; Ruibo Xiong; Yongrong Wu; Jingan Chen; Lifan Chen; Pengfei Ding; Fan Lin; |
| 381 | Bridging The Gap Between Sparsity and Redundancy: A Dual-Decoding Framework with Global Context for Map Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, uneven trajectory density often leads to fragmented roads in sparse areas and redundant segments in dense regions, posing significant challenges for existing methods. To address these issues, we propose DGMap, a dual-decoding framework with global context awareness, featuring Multi-scale Grid Encoding, Mask-enhanced Keypoint Extraction, and Global Context-aware Relation Prediction. |
Yudong Shen; Jiali Mao; Wenyu Wu; Yixiao Tong; Guoping Liu; Chaoya Wang; |
| 382 | Full-Atom Protein-Protein Interaction Prediction Via Atomic Equivariant Attention Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel model, MEANT, designed to adaptively extract atom-level geometric information from varying numbers of atoms within different residues for PPI prediction. |
Chunchen Wang; Cheng Yang; Wenchuan Yang; Le Song; Chuan Shi; |
| 383 | Improving Graph Autoencoders By Hard Sample Refinement with Global Similarity Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing feature-based GAEs face performance bottlenecks, particularly on hard-to-reconstruct nodes, due to their excessive reliance on local aggregation. To address this limitation, we propose a novel framework, Global-Similarity-Enhanced Graph Autoencoder (GSE-GAE). |
Ge Chen; Yulan Hu; Sheng Ouyang; Cuicui Luo; |
| 384 | Local Large Language Models for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose Local Large Language Models for Recommendation(L3Rec), a novel model-agnostic framework that integrates collaborative filtering(CF) with generative LLMs through localized modeling. |
Yujin Jeon; Jooyoung Kim; Joonseok Lee; |
| 385 | TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose TANDEM (Temporal Attention-guided Neural Differential Equations for Missingness), an attention-guided neural differential equation framework that effectively classifies time series data with missing values. |
Yongkyung Oh; Dongyoung Lim; Sungil Kim; Alex A.T. Bui; |
| 386 | CEM: A Data-Efficient Method for Large Language Models to Continue Evolving From Mistakes Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these, we propose the Continue Evolving from Mistakes (CEM) method, a novel and data-efficient framework for continuous LLM evolution. |
Haokun Zhao; Jinyi Han; Jie Shi; Chengyu Du; Jiaqing Liang; Yanghua Xiao; Weikang Zhou; Zeye Sun; Fei Yu; |
| 387 | Strong Forgetting for ALCQ-Ontologies Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present the first method for strong role forgetting in description logics with qualified number restrictions (Q ). |
Sen Wang; Yizheng Zhao; |
| 388 | From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce Chain-of-Intent, a novel framework that integrates Hidden Markov Models (HMMs) with Large Language Models (LLMs) to generate intent-driven, context-aware dialogues through self-play. |
Junhua Liu; Yong Keat Tan; Bin Fu; Kwan Hui Lim; |
| 389 | EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods face two critical yet under-explored issues: Structural Visit Bias, where random walk sampling disproportionately emphasizes high-degree nodes, leading to redundant and noisy structural representations; and Abrupt Evolution Blindness, the failure to effectively detect sudden structural changes due to rigid or overly simplistic temporal modeling strategies, resulting in inconsistent temporal embeddings. To overcome these challenges, we propose EvoFormer, an evolution-aware Transformer framework tailored for dynamic graph-level representation learning. |
Haodi Zhong; Liuxin Zou; Di Wang; Bo Wan; Zhenxing Niu; Quan Wang; |
| 390 | TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Conventional approaches introduce sequential dependencies that impede efficient retrieval, as they are inherently unsuitable for generating multiple items without positional constraints within a single request session. To address these limitations, we propose TBGRecall, a framework integrating Next Session Prediction (NSP), designed to enhance generative retrieval models for e-commerce applications. |
Zida Liang; Changfa Wu; Dunxian Huang; Weiqiang Sun; Ziyang Wang; Yuliang Yan; Jian Wu; Yuning Jiang; Bo Zheng; Ke Chen; Silu Zhou; Yu Zhang; |
| 391 | Stratified Expert Cloning for Retention-Aware Recommendation at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce Stratified Expert Cloning (SEC), an imitation learning framework that leverages abundant interaction data from high-retention users to learn robust policies. |
Chengzhi Lin; Annan Xie; Shuchang Liu; Wuhong Wang; Chuyuan Wang; Yongqi Liu; Han Li; |
| 392 | Gravity-GNN: Deep Reinforcement Learning Guided Space Gravity-based Graph Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, most existing GNNs are highly sensitive to neighborhood aggregation, and irrelevant information in the graph topology can lead to inefficient or even invalid node embeddings. To overcome these challenges, we propose a novel Space Gravity-based Graph Neural Network (Gravity-GNN) guided by Deep Reinforcement Learning (DRL). |
Huaming Wu; Lei Tian; Chaogang Tang; Pengfei Jiao; Minxian Xu; Huijun Tang; |
| 393 | Exploring Diverse Sparse Network Structures Via Dynamic Pruning with Weight Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a new method to maximize the effect of finding sparse patterns through a gradient scaling technique that modifies the weight distribution. |
Jinwoo Kim; Jongyun Shin; Sangho An; Jangho Kim; |
| 394 | Point-DMAE: Point Cloud Self-supervised Learning Via Density-directed Masked Autoencoders Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This uneven distribution suggests that the application of random masking strategies, commonly adopted from NLP and 2D vision, may not be optimal for point cloud data, potentially leading to suboptimal learning outcomes. Based on this observation, we propose a simple yet effective Density-directed Masked Autoencoders for Point Cloud Self-supervised Learning (Point-DMAE), which learns latent semantic point cloud features using a density-directed masking strategy. |
Xianglong Jin; Zheng Wang; Wenjie Zheng; Feiping Nie; |
| 395 | Constructing Set-Compositional and Negated Representations for First-Stage Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Specifically, we introduce Disentangled Negation that penalizes only the negated parts of a query, and a Combined Pseudo-Term approach that enhances LSR’s ability to handle intersections. |
Antonios Minas Krasakis; Andrew Yates; Evangelos Kanoulas; |
| 396 | Spatio-Temporal Wavelet Enhanced Attention Mamba for Stock Price Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose STEAM, a Spatio-Temporal Wavelet Enhanced Attention Mamba model. |
Shurui Wang; Wenbo Yan; Ying Tan; |
| 397 | PRIMA: Privacy Preserving Multi-dimensional Analytic Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we focus on the process of answering sum queries over data cube, each of which consists of a collection of cuboids, while satisfying differential privacy (DP). |
Yufei Wang; Xiang Cheng; Pengfei Zhang; Anxing Wei; |
| 398 | Entity-Aware Generative Retrieval for Personalized Contexts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose PEARL (Personalized Entity-Aware Generative RetrievaL), a novel generative retrieval framework for personalized IR. |
Jihyeong Jeon; Jiwon Lee; Cheol Ryu; U Kang; |
| 399 | THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, despite their power, general-purpose LLM embedding models are not well-suited to capture the nuanced characteristics of financial assets, since the semantic representation of investment assets may differ fundamentally from that of general financial text. To address this, we introduce THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning. |
Hoyoung Lee; Wonbin Ahn; Suhwan Park; Jaehoon Lee; Minjae Kim; Sungdong Yoo; Taeyoon Lim; Woohyung Lim; Yongjae Lee; |
| 400 | ReportGRI: Automating GRI Alignment and Report Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Such disparity, together with report complexity and volume, poses significant challenges to transparency, comparability and standardisation. To address this problem, we introduce ReportGRI, an automated system for Global Reporting Initiative (GRI) indexing and qualitative assessment of CSRs. |
Aida Usmanova; Rana Abdullah; Debayan Banerjee; Markus Leippold; Ricardo Usbeck; |
| 401 | CoinCLIP: A Multimodal Framework for Assessing Viability in Web3 Memecoins Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This overwhelming influx of speculative tokens creates a challenge in distinguishing viable memecoins from those that are unlikely to succeed. To address this issue, we introduce CoinVibe, a comprehensive multimodal dataset designed to evaluate the viability of memecoins. |
Hou-Wan Long; Hongyang Li; Wei Cai; |
| 402 | CAGCL: A Community-Aware Graph Contrastive Learning Model for Social Bot Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a new Community-Aware Graph Contrastive Learning (CAGCL) framework for enhanced social bot detection. |
Kaihang Wei; Min Teng; Haotong Du; Songxin Wang; Jinhe Zhao; Chao Gao; |
| 403 | FakeChain: Exposing Shallow Cues in Multi-Step Deepfake Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce FakeChain, a large-scale benchmark comprising 1-, 2-, and 3-Step forgeries synthesized using five state-of-the-art representative generators. |
Minji Heo; Simon S. Woo; |
| 404 | SCAlign: Transaction Event Prediction Via Multi-Scale Market Dynamics Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Therefore, these phenomena pose significant challenges to traditional event modeling approaches that rely solely on consumer behaviors. To address this problem, we propose SCAlign, a cross-domain and multi-scale framework for market dynamics alignment, designed for event prediction. |
Boyang Li; Lingzheng Zhang; Fugee Tsung; Xi Zhang; |
| 405 | Content-Agnostic Moderation for Stance-Neutral Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While content-aware moderation has been proposed to mitigate these effects, such approaches risk curtailing the freedom of speech and information. To address this concern, we propose and explore the feasibility of content-agnostic moderation as an alternative approach for reducing polarization. |
Nan Li; Bo Kang; Tijl De Bie; |
| 406 | AR2: Adversarial Reinforcement Learning for Abstract Reasoning in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this study, we propose AR2 (Adversarial Reinforcement Learning for Abstract Reasoning), a novel framework explicitly designed to enhance the abstraction abilities of LLMs. |
Cheng-Kai Yeh; Hsing-Wang Lee; Chung-Hung Kuo; Hen-Hsen Huang; |
| 407 | Robust Handwritten Text Recognition Via Multi-Source Adversarial Domain Adaptation for Low-Resource Scripts Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce two novel multi-adversarial adaptation strategies: (1) an Additive strategy that jointly optimizes a domain classification loss and a prototype-based alignment loss, and (2) an Integrative strategy that uses a prototype-driven adversarial signal to augment the domain classifier with semantic constraints. |
Bustami Yusuf; Hen-Hsen Huang; |
| 408 | Streamlining Feature Interactions Via Selectively Crossing Vectors for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This suggests that most interactions are unnecessary. Motivated by this finding, we propose SCV: Selectively Crossing Vectors, a CTR prediction framework that reformulates feature interaction learning as a sparse edge selection task over a globally shared feature-interaction graph. |
Byungwoo Jang; Jinhee Park; Eunil Park; |
| 409 | Monte Carlo Tree Search for Graph Reasoning in Large Language Model Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In domains such as scientific publishing, entities like papers, authors, and citations form rich graphs, where meaning emerges not only from individual texts but also from their relationships. To address this, we propose Graph-MCTS, a framework that enhances LLM reasoning by leveraging graph structures. |
Lihui Liu; |
| 410 | Learning Conditional Probability Distributions for Robust Probabilistic Inference in Bayesian Network Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we incorporate the idea of learning and search for robust probabilistic inferences in BN. |
Xinran Wu; Kun Yue; Huashuai Liu; Liang Duan; |
| 411 | Multimodal RAG Enhanced Visual Description Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although fine-tuning can potentially mitigate this gap, it is typically expensive and impractical due to the requirement for extensive domain-driven data. To overcome this challenge, we propose a lightweight training-free approach utilising Retrieval-Augmented Generation (RAG) to extend across the modality using a linear mapping, which can be computed efficiently. |
Amit Kumar Jaiswal; Haiming Liu; Ingo Frommholz; |
| 412 | Multimodal Banking Dataset: Understanding Client Needs Through Event Sequences Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: Despite the urgent practical need, developing deep learning techniques suitable to handle such data is limited by the absence of large open-source multi-source real-world datasets of event sequences. To fill this gap, which is mainly caused by security reasons, we present the first industrial-scale publicly available multimodal banking dataset, MBD, that contains information on more than 2M corporate clients of a large bank. |
Dzhambulat Mollaev; Ivan Kireev; Mikhail Orlov; Alexander Kostin; Ivan Karpukhin; Maria Postnova; Gleb Gusev; Andrey Savchenko; |
| 413 | Frequency-Decoupled Distillation for Efficient Multimodal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, traditional knowledge distillation methods struggle to transfer knowledge effectively from MMRec to MLPRec, due to differences in their model structure and capacity. To address this, we propose a frequency-decoupled knowledge distillation framework-FDRec-to efficiently transfer knowledge from MMRec to MLPRec. |
Ziyi Zhuang; Hongji Li; Junchen Fu; Jiacheng Liu; Joemon M. Jose; Youhua Li; Yongxin Ni; |
| 414 | Dual-Space Masked Reconstruction for Robust Self-Supervised Human Activity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Human Activity Recognition (HAR) based on wearable sensors faces critical challenges, including limited labeled data, distribution shifts, and sensitivity to sensor noise. To address these issues, this paper proposes a novel self-supervised learning (SSL) framework that leverages dual-space masked reconstruction to learn robust and generalizable representations for sensor-based HAR. |
Shuo Xiao; Jiukai Deng; Chaogang Tang; Zhenzhen Huang; |
| 415 | Frequency-Domain Disentanglement-Fusion and Dual Contrastive Learning for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In addition to the inherent limitations of self-attention mechanisms, sequential recommendation faces persistent challenges such as data sparsity and noise. To address these issues, we propose Frequency-Domain Disentanglement-Fusion and Dual Contrastive Learning for Sequential Recommendation (FDCLRec). |
Shuo Xiao; Jingtao Zhang; Chaogang Tang; Zhenzhen Huang; |
| 416 | Decoupling Feature Entanglement for Personalized Federated Learning Via Neural Collapse Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Motivated by the phenomenon of Neural Collapse (NC) observed in well-trained deep classification models, we propose FedDemux, a novel pFL framework that facilitates the personalization process by explicitly promoting the emergence of NC. |
Haizhou Du; Pengfei Li; |
| 417 | When Variety Seeking Meets Multi-Sided Recommendation Fairness: A Consistent and Personalized Multi-Objective Optimization Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This issue is inherently challenging due to the competing goals of different stakeholders. To tackle this challenge, we propose a Consistent and Personalized Fairness Recommendation framework with Multi-Objective Integer Programming (CPFR-MOIP). |
Jiayi Guo; Jiangning He; Chenyan Wang; Xinran Wu; |
| 418 | RAG-based Unanswerable Question Detection in Clinical Text-to-SQL Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This task is challenging due to data imbalance, and existing methods are computationally expensive and inflexible to data distribution changes. To address these issues, we propose Retrieval-augmented Question Answerability Detection (RaQAD), a training-free method that uses LLMs to identify unanswerable questions by retrieving semantically similar examples as few-shot prompts. |
Donghee Han; Seungjae Lim; Mun Yong Yi; |
| 419 | LinkedIn Post Embeddings: Industrial Scale Embedding Generation and Usage Across LinkedIn Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper presents the post embeddings used at LinkedIn, where a pre-trained transformer-based large language model (LLM) is taken as input and fine-tuned using multi-task learning across a diverse set of semantic labeling tasks. |
Sudarshan Srinivasa Ramanujam; Akanksha Bindal; Yu Jiang; Timothy J. Hazen; David Golland; Fengyu Zhang; Daqi Sun; Wanning Li; Birjodh Singh Tiwana; Siddharth Dangi; Peng Yan; |
| 420 | Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corpora Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: A classifier using byte n-grams as features is the only approach we have found fast enough to meet requirements in size (sub 2 MB), speed (multiple GB/s), and latency (sub 10 ms) for deployment in numerous malware detection scenarios. |
Edward Raff; Ryan R. Curtin; Derek Everett; Robert J. Joyce; James Holt; |
| 421 | ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although multi-task learning is widely adopted to jointly optimize DT and CTR, we observe that multi-task models systematically collapse their DT predictions to the shortest and longest bins, under-predicting the moderate durations. We attribute this moderate-duration bin under-representation to over-reliance on the CTR-DT spurious correlation, and propose ORCA to address it with causal-decoupling. |
Huishi Luo; Fuzhen Zhuang; Yongchun Zhu; Yiqing Wu; Bo Kang; Ruobing Xie; Feng Xia; Deqing Wang; Jin Dong; |
| 422 | Enhancing Multi-Behavior Sequential Recommenders with Behavior-Aware Regularization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we first analyze the learning distribution of MBSR, shedding light on the significance of target behavior in next-item prediction. Building upon this insight, we propose a Behavior-Aware Regularization approach for multi-behavior sequential Rec ommendation (BAR4Rec), where we introduce a regularization loss function to preserve the intrinsic constraints of target behavior. |
Yongfu Fan; Jin Chen; Yangzixuan Jiao; Ximu Zeng; Liwei Deng; Kai Zheng; |
| 423 | FinCast: A Foundation Model for Financial Time-Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While recent deep learning methods attempt to address these complexities, they frequently suffer from overfitting and typically require extensive domain-specific fine-tuning. To overcome these limitations, we introduce FinCast, the first foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets. |
Zhuohang Zhu; Haodong Chen; Qiang Qu; Vera Chung; |
| 424 | AutoDW-TS: Automated Data Wrangling for Time-Series Data Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we introduce AutoDW-TS, an automated approach to time-series data wrangling powered by Large Language Models (LLMs). |
Lei Liu; So Hasegawa; Shailaja Keyur Sampat; Mehdi Bahrami; Wei-Peng Chen; Kodai Toyota; Takashi Kato; Takumi Akazaki; Akira Ura; Tatsuya Asai; |
| 425 | InstANNS: Scalable Approximate Nearest Neighbor Search Via Cost-Efficient In-Storage Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing disk-augmented indexing systems, such as SPANN, often face performance bottlenecks due to limited host interface bandwidth, typically constrained by PCIe. To address this bottleneck, we introduce InstANNS, a storage-centric ANNS architecture that improves throughput and reduces data transfer by performing query-aware PQ filtering inside SSDs, without relying on GPUs. |
Bonggeun Sim; Yushin Kim; Minseo Kim; Yeonhong Park; Jae W. Lee; |
| 426 | HUSK: A Hierarchically Structured Urban Knowledge Graph Dataset for Multi-Level Spatial Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a Hierarchically Structured UrbanKG Dataset (HUSK) with an intermediate functional zone layer that bridges and enriches the understanding across multiple levels, and evaluate it on three area-level and three POI-level tasks, showing accuracy improvements over single-view baselines. |
Qiqi Wang; Guanjin Wang; Yihong Pan; Zhipeng Lin; Huijia Li; Qian Liu; Kaiqi Zhao; |
| 427 | Hearable Image: On-Device Image-Driven Sound Effect Generation for Hearing What You See Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a robust on-device sound effect generation framework that is image-to-audio generation based on latent diffusion. |
Deokjun Eom; Nahyun Kim; Woohyun Nam; Kyung-Rae Kim; Chaebin Im; Jungwon Park; |
| 428 | DPT: Dynamic Preference Transfer for Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods have two limitations: 1) When transferring user’s preferences from the source domain, they encode preferences into a static and holistic representation, ignoring the rich information inherent in the dynamic evolution of user preferences over time; 2) They adopt a distribution-agnostic full-transfer strategy, failing to effectively limit the transfer degree of source-domain preferences according to different data distributions, which poses a risk of negative transfer. To address these issues, we propose the Dynamic Preference Transfer (DPT) model. |
Xiang Ying; Rui Ding; Yue Zhao; Mei Yu; Mankun Zhao; |
| 429 | Externalizing Social-Cognitive Structures for User Modeling: Toward Theory-Driven Profiling with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose TRIPLE (TPB-dRIven Profiling with LLM rEfinement), a dynamic profiling framework that incorporates the Theory of Planned Behavior (TPB) into user profile modeling. |
Taehyung Noh; Seungwan Jin; Haein Yeo; Kyungsik Han; |
| 430 | TSD-CT: A Benchmark Dataset for Truthfulness Stance Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present TSD-CT (Truthfulness Stance Detection-Claim and Tweet), a benchmark dataset designed to advance research in truthfulness stance detection. |
Zhengyuan Zhu; Haiqi Zhang; Zeyu Zhang; Chengkai Li; |
| 431 | TrustMap: Mapping Truthfulness Stance of Social Media Posts on Factual Claims for Geographical Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present TrustMap, an application that identifies and visualizes stances of tweets toward factual claims. |
Zhengyuan Zhu; Haiqi Zhang; Zeyu Zhang; Chengkai Li; |
| 432 | Spreader Behavior Forecasting: Intent-aware Neural Processes for Intervening Misinformation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a novel Intent-aware Neural Processes (INP) model, which focuses on tracking the evolving intent of spreaders over time. |
Haoran Chen; Dongmei Han; |
| 433 | EvalAgent: Towards Evaluating News Recommender Systems with LLM-based Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional offline evaluation methods struggle with evolving user behavior and dynamic system adaptation, while online experiments are costly, time-consuming, and ethically challenging. To address these challenges, this paper introduces EvalAgent, a large language model agent system for simulating real-world online news recommender systems. |
Guangping Zhang; Peng Zhang; Jiahao Liu; Zhuoheng Li; Dongsheng Li; Hansu Gu; Tun Lu; Ning Gu; |
| 434 | FinD3: A Dual 3D State Space Model with Dynamic Hypergraph for Financial Stock Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although some recent approaches claim to model 3D Multivariate Time Series (3D-MTS) dependencies, they often discard substantial information and fail to capture the dynamics of the stock market. To address these limitations, we propose FinD3, a Financial 3D model using Dual cubic state spaces and Dynamic hypergraphs. |
Jieyuan Mei; Jindong Tian; Ronghui Xu; Hanyue Wei; Chenjuan Guo; Bin Yang; |
| 435 | Land Deformation Prediction Via Multi-modal Adaptive Association Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing prediction methods face two major challenges: cross-area association bottleneck and inadequate handling of temporal distribution heterogeneity. To address these challenges, we propose Multi-modal Adaptive Association Learning framework (MAAL). |
Wanghui Qiu; Shiyan Hu; Chenjuan Guo; Wenbing Shi; Lina Yu; Ming Gao; Aoying Zhou; Bin Yang; |
| 436 | Aggregated Gradients-based Adaptive Learning Rate Design in Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In our study, we present a novel algorithm designed to alleviate client drifting on Non-IID data and enhance model performance, termed FedAgile (Aggregated Gradients-based AdaptIve LEarning Rate Design in FEDerated Learning), which designs the adaptive learning rate by introducing an aggregated gradient term to accelerate model convergence and mean-field terms to approximate the average local information over time. |
Wenhao Yuan; Xuehe Wang; |
| 437 | Query, Decompose, Compress: Structured Query Expansion for Efficient Multi-Hop Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, their generative nature often undermines performance on complex multi-hop retrieval tasks by introducing irrelevant or noisy information. To address this challenge, we propose DeCoR (Decompose and Compress for Retrieval), a framework grounded in structured information refinement. |
Jungmin Yun; Youngbin Kim; |
| 438 | Rethinking The Training Paradigm of Discrete Token-Based Multimodal LLMs: An Analysis of Text-Centric Bias Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We identify a structural limitation inherent in this training paradigm, termed text-centric bias, defined as an over-reliance on the textual context that restricts intrinsic modality understanding. To systematically analyze the existence of this bias, we propose an analytical framework involving external perplexity-based and internal neuron-level analyses. |
Wansik Jo; Jooyeong Na; Soyeon Hong; Seungtaek Choi; Hyunsouk Cho; |
| 439 | CALLM: A Framework for Systematic Contrastive Analysis of Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study addresses the challenges of analyzing discrepancies between different large language models (LLMs). To facilitate the automatic exploration of these differences, we propose a novel system called CALLM(Contrastive Analyzer of LLMs) that systematically compares the outputs of two LLM versions based on user-defined queries. |
Reinhard Fritsch; Adam Jatowt; |
| 440 | Guess The Age of Photos: An Interactive Web Platform for Historical Image Age Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces Guess the Age of Photos, a web platform engaging users in estimating the years of historical photographs through two gamified modes: Guess the Year (predicting a single image’s year) and Timeline Challenge (comparing two images to identify the older). |
Hasan Y\{u}cedag; Adam Jatowt; |
| 441 | LHMformer:Long-Range Historical Memory-Enhanced Transformer for Traffic Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We revisited the synergistic effects of Transformer-based models and TCN-MLP models in spatio-temporal data forecasting and proposed the Long-Range Historical Memory-Enhanced transformer (LHMformer). |
Guoping Qi; Jihong Liu; |
| 442 | LLM-Powered Information Extraction for The Dairy Financial Domain: Tackling Data Scarcity and Ambiguity Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Traditional methods often fail to capture rare patterns, struggle with vague mentions, and exhibit poor generalization in low-resource settings. To address these issues, we propose a novel framework that integrates large language models (LLMs) with targeted data augmentation and agent-based retrieval-augmented generation (RAG). |
Chunyan An; Yuying Huang; Qiang Yang; Siyu Yuan; Zhixu Li; |
| 443 | Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models Via Model Editing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address these challenges, we establish new standards for an effective and feasible backdoor attack in the context of large pre-trained models. In line with these standards, we introduce our EDT model, an Efficient, Data-free, Training-free backdoor attack method. |
Dongliang Guo; Mengxuan Hu; Zihan Guan; Junfeng Guo; Thomas Hartvigsen; Sheng Li; |
| 444 | E2MoCase: A Dataset for Emotional, Event and Moral Observations in News Articles on High-impact Legal Cases Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we introduce E2MoCase, a novel dataset that enables integrated analysis of emotions, morality, and events within legal narratives and media coverage. |
Candida M. Greco; Lorenzo Zangari; Davide Picca; Andrea Tagarelli; |
| 445 | Balance and Brighten: A Twin-Propeller Network to Release Potential of Physics Laws for Traffic State Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Secondly, existing methods implicitly employ physics laws as auxiliary terms, which ignores that explicitly utilizing certain properties of physics laws can compensate for the shortcomings of data-driven models, particularly with regard to the data noise and relationships between variables. To alleviate these issues, we propose a Twin-Propeller Network (TPN) to realize fully message exchange among physical knowledge and data information, that releases the potential of the physics laws. |
Weihao Jiang; Yao Fu; Hong Zhao; Xiaoyu Cai; Ruiheng Yang; Linsen Li; Jiang Zhu; |
| 446 | MIRAGE: A Metrics LIbrary for Rating HAllucinations in Generated TExt Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While several automatic metrics have been proposed to detect and quantify hallucinations, such as FactCC, QAGS, FEQA, and FactAcc, these metrics are often unavailable, difficult to reproduce, or incompatible with modern development workflows. We introduce MIRAGE, an open-source Python library designed to address these limitations. |
Benjamin Vendeville; Liana Ermakova; Pierre De Loor; Jaap Kamps; |
| 447 | GFlowNet with Gradient-based Optimization for Bayesian Network Structure Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although posterior approximation methods can quantify the epistemic uncertainty over the learned BN structures, they cannot reliably identify the optimal BN structures. To tackle these issues, we propose a GFlowNet with Gradient-based Optimization (GFlowOpt) for the BN structure learning method. |
Zhu Yang; Kun Yue; Zhiwei Qi; Liang Duan; Jianyu Li; |
| 448 | AI on The Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced AI models. |
Davide Gabrielli; Bardh Prenkaj; Paola Velardi; Stefano Faralli; |
| 449 | Amortized Baseline Selection Via Rank-Revealing QR for Efficient Model Explanation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Existing acceleration approaches either lack a theoretical base or provide no principled guidance for baseline selection. To address this gap, we present ABSQR (Amortized Baseline Selection via Rank-Revealing QR). |
Chanwoo Lee; Youngjin Park; Hyeongeun Lee; Yeeun Yoo; Daehee Han; Junho Choi; Geonhyeong Kim; Nari Kim; Jaesik Choi; |
| 450 | A Demonstration of PKGem: Secure Enrichment of Personal Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present PKGem, a system that provides an end-to-end secure solution to enrich personal knowledge graphs in mobile environments. |
Junzhou Su; Sriram Nutulapati; Chang Ge; |
| 451 | Densest Subgraph Discovery on Decentralized Graphs with Local Edge Differential Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose a new LEDP algorithm for DSD that utilizes the Randomized Response (RR) mechanism for user-side perturbation and extends greedy peeling with degree correction to find the densest subgraph on the server-side noisy global graph. |
Wenping Tong; Yi Zhou; Yanhao Wang; Cen Chen; Minghao Zhao; |
| 452 | FinS-Pilot: A Benchmark for Online Financial RAG System Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, the development of financial RAG benchmarks has been constrained by data confidentiality issues and the lack of dynamic data integration. To address this issue, we introduce FinS-Pilot, a novel benchmark for evaluating RAG systems in online financial applications. |
Feng Wang; Yiding Sun; Jiaxin Mao; Xue Wei; Danqing Xu; |
| 453 | Upcycling Candidate Tokens of Large Language Models for Query Expansion Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recently, Large Language Models (LLMs) have been used for QE, but existing methods face a trade-off: generating diverse terms boosts performance but increases computational cost. To address this challenge, we propose Candidate Token Query Expansion (CTQE), which extracts diverse and relevant terms from a single LLM decoding pass by leveraging unselected candidate tokens. |
Jinseok Kim; Sukmin Cho; Soyeong Jeong; Sangyeop Kim; Sungzoon Cho; |
| 454 | Curriculum Guided Personalized Subgraph Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To this end, we propose a novel personalized subgraph FL framework called Curriculum Guided Personalized SUbgraph Federated Learning (CUFL). |
Minku Kang; Hogun Park; |
| 455 | DDE-CLIP: Detail-Guided Dual-Modal Enhancement for Zero-Shot Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Furthermore, most existing methods employ fixed text prompt to guide the model, which is difficult to describe diverse and unseen anomalies, leading to poor accuracy. To address these limitations, we propose a Detail-guided Dual-modal Enhancement Model (DDE-CLIP) for the ZSAD task. |
Zehao Deng; Qingzhi Ma; An Liu; |
| 456 | TKHist: Cardinality Estimation for Join Queries Via Histograms with Dominant Attribute Correlation Finding Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While recent advancements have significantly improved the accuracy of multi-table join query estimations, these methods introduce challenges such as higher space overhead, increased latency, and greater complexity, especially when integrated with the binary join framework. In this paper, we introduce a novel cardinality estimation method named TKHist, which addresses these challenges by relaxing the uniformity assumption in histograms. |
Renrui Li; Qingzhi Ma; Jiajie Xu; Lei Zhao; An Liu; |
| 457 | Federated Continual Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: As a solution, we propose F3CRec, a framework designed to balance knowledge retention and adaptation under the strict constraints of FCRec. |
Jaehyung Lim; Wonbin Kweon; Woojoo Kim; Junyoung Kim; Seongjin Choi; Dongha Kim; Hwanjo Yu; |
| 458 | Time-Period-Aware Embedding Regeneration for Session-Based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Session-based recommender systems typically focus on intra-session user behavior but often overlook the macro-level temporal evolution of items themselves. To address this gap, we introduce a model that explicitly captures item dynamics by regenerating time-period-aware embeddings. |
Cheng Guo; Rui Xue; Jeff Zhang; |
| 459 | FROG: Fair Removal on Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For instance, forgetting links between users of different genders may inadvertently exacerbate group disparities. To address this issue, we propose a novel framework that jointly optimizes both the graph structure and the model to achieve fair unlearning. |
Ziheng Chen; Jiali Cheng; Hadi Amiri; Kaushiki Nag; Lu Lin; Sijia Liu; Gabriele Tolomei; Xiangguo Sun; |
| 460 | Beyond Pairwise Learning-To-Rank At Airbnb Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: But here’s the catch—no algorithm can achieve all three at the same time. |
Malay Haldar; Daochen Zha; Huiji Gao; Liwei He; Sanjeev Katariya; |
| 461 | Multimodal Sentiment Analysis with Multi-Perspective Thinking Via Large Multimodal Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Recent progress in large multimodal models (LMMs) has demonstrated their impressive reasoning abilities, which can be leveraged to improve traditional MSA approaches by providing a deeper understanding of the sematic connection of the modalities. Toward this issue, in this paper, we propose a novel framework called MPT that combines traditional MSA approaches with Multi-Perspective Thinking from LMMs to improve prediction outcomes. |
Juhao Ma; Shuai Xu; Yicong Li; Xiaoming Fu; |
| 462 | FedFMD: Fairness-Driven Adaptive Aggregation in Federated Learning Via Mahalanobis Distance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: These approaches inadequately capture the intrinsic impact of Non-IID data characteristics on model divergence. To address these deficiencies, we propose a novel adaptive weight allocation algorithm, FedFMD, leveraging Mahalanobis distance, integrating Task Arithmetic, to dynamically assign weights based on client contributions. FedFMD explicitly models task-centric deviations caused by data heterogeneity without requiring raw data access or prior distribution assumptions. |
Xiuting Weng; Lixing Yu; Shaojie Zhan; Ruizhi Pu; Xiaofei Liu; |
| 463 | Multimodal Contrastive Learning with Early Fusion for Robust Medical Signal Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This leads to suboptimal representations that fail to capture complex interdependencies across modalities. To address this limitation, we propose a multimodal contrastive learning framework that aligns fused representations instead of individual signals. |
Lei Chen; Kyoungsuk Park; Junetae Kim; |
| 464 | UniROM: Unifying Online Advertising Ranking As One Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we present UniROM, an end-to-end generative architecture that Unifies online advertising Ranking as One Model. |
Junyan Qiu; Ze Wang; Fan Zhang; Zuowu Zheng; Jile Zhu; Jiangke Fan; Teng Zhang; Haitao Wang; Xingxing Wang; |
| 465 | DIVAgent: A Diversified Search Agent That Mimics The Human Search Process Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Inspired by how humans explore diverse information during real-world searching, we propose a diversified search agent DIVAgent to combine the advantages of supervised and unsupervised methods. |
Zhirui Deng; Jingfen Qiao; Zhicheng Dou; Ji-Rong Wen; Maarten de Rijke; |
| 466 | CSRM-LLM: Embracing Multilingual LLMs for Cold-Start Relevance Matching in Emerging E-commerce Markets Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we share our experiences in Coupang to provide a competitive cold-start performance of relevance matching for emerging e-commerce markets. |
Yujing Wang; Yiren Chen; Huoran Li; Chunxu Xu; Yuchong Luo; Xianghui Mao; Cong Li; Lun Du; Chunyang Ma; Qiqi Jiang; Yin Wang; Fan Gao; Wenting Mo; Pei Wen; Shantanu Kumar; Taejin Park; Yiwei Song; Vijay Rajaram; Tao Cheng; Sonu Durgia; Pranam Kolari; |
| 467 | Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Conventional fine-tuning methods suffer from catastrophic forgetting and substantial resource consumption, while existing parameter-efficient methods perform suboptimally in complex multi-task scenarios. To address this, we propose Contextual Attention Modulation (CAM), a novel mechanism that dynamically modulates the representations of self-attention modules in LLMs. |
Dayan Pan; Zhaoyang Fu; Jingyuan Wang; Xiao Han; Yue Zhu; Xiangyu Zhao; |
| 468 | An Embarrassingly Simple But Effective Knowledge-enhanced Recommender Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While contrastive learning has emerged as a powerful paradigm for integrating these dual information sources, we identify a critical limitation in existing approaches: current methods fail to effectively balance the contrastive views derived from IG and KG, often resulting in performance degradation compared to using IG alone. To address this fundamental challenge, we propose SimKGCL, a novel contrastive learning framework that introduces a simple yet principled solution — cross-view, layer-wise fusion between IG and KG representations prior to contrastive learning. |
Haibo Ye; Lijun Zhang; Yuan Yao; XinJie Li; |
| 469 | Towards Instance-wise Personalized Federated Learning Via Semi-Implicit Bayesian Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This assumption often fails in practice, where a single client may possess data from multiple sources or domains, resulting in significant intra-client heterogeneity and suboptimal performance. To tackle this challenge, we propose pFedBayesPT, a fine-grained instance-wise pFL framework based on visual prompt tuning. |
Tiandi Ye; Wenyan Liu; Kai Yao; Lichun Li; Shangchao Su; Cen Chen; Xiang Li; Shan Yin; Ming Gao; |
| 470 | JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This study introduces JustEva, a comprehensive, open-source evaluation toolkit designed to measure LLM fairness in legal tasks. |
Zongyue Xue; Siyuan Zheng; Shaochun Wang; Yiran Hu; Yuxin Yao; Shengran Wang; Haitao Li; Qingyao Ai; Yiqun Liu; Yun Liu; Weixing Shen; |
| 471 | Can LLMs Really Help Query Understanding In Web Search? A Practical Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we investigate the potential of LLMs in query understanding by conducting a comprehensive evaluation across three dimensions: term, structure, and topic. |
Dezhi Ye; Ye Qin; Bowen Tian; Jiabin Fan; Jie Liu; Haijin Liang; Jin Ma; |
| 472 | Tilia: Enhancing LIME with Decision Tree Surrogates Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: While prior work has proposed techniques to enhance LIME, they remain fundamentally limited by the expressiveness of linear surrogate models, which cannot adequately capture complex decision boundaries. In this work, we introduce Tilia, a novel method that employs shallow decision tree regressors as the surrogate model, leveraging its structured and deterministic nature to improve both fidelity and stability. |
Jihang Li; Jiacheng Qiu; Yin-Ping Zhao; Zeyi Wen; |
| 473 | From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose PostToPersonality (P2P), a novel LLM- based framework for MBTI prediction from social media posts of individuals. |
Tian Ma; Kaiyu Feng; Yu Rong; Kangfei Zhao; |
| 474 | ThoughtForest-KGQA: A Multi-Chain Tree Search for Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Most multi-hop Knowledge Graph Question Answering (KGQA) methods utilize fixed pruning strategies that, while efficient, critically impair the diversity of answer paths and fail to discover complex or less common correct answers. To address these limitations, this paper introduces ThoughtForest-KGQA, a novel multi-chain tree search algorithm. |
Xingrun Quan; Yongkang Zhou; Junjie Yao; |
| 475 | A Soft-partitioned Semi-supervised Collaborative Transfer Learning Approach for Multi-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: (2) Overfitting: Sparse data in non-dominant domains leads to overfitting in specific parameters. To tackle these challenges, we propose Soft-partitioned Semi-supervised Collaborative Transfer Learning (SSCTL) for multi-domain recommendation. |
Liu Xiaoyu; Yiqing Wu; Ruidong Han; Fuzhen Zhuang; Xiang Li; Wei Lin; |
| 476 | DT-FedSDC: A Dual-Target Federated Framework with Semantic Enhancement and Disentangled Contrastive Learning for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper proposes a dual-target federated cross-domain recommendation framework with semantic enhancement and disentangled contrastive learning. |
Shanyang Gao; Shanfeng Wang; Lanyu Yao; Jianzhao Li; Zhao Wang; Maoguo Gong; Ke Pan; |
| 477 | Audience-Aware and Self-Adaptive Multi-Interest Modeling for Sharing Rate Prediction in Affiliate Marketing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: For Challenge (1), we employ a dynamic routing mechanism based on interest capsules to model the diverse interests of promoters, where audience groups are used to optimize the interest routing via a novel dual-channel attention mechanism, thus allowing audience groups to explicitly participate in the promoter decision-making process with an auxiliary role. |
Zhe Wang; Ziyu Guan; Yujian Cao; Yaming Yang; Rui Wang; Bin Tong; Wei Zhao; Hongbo Deng; |
| 478 | Dynamic Network-Based Two-Stage Time Series Forecasting for Affiliate Marketing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Unfortunately, existing time series forecasting techniques fail to deliver accurate predictions due to the propagation scale being influenced by multiple factors and the inherent complexities arising from dynamic scenarios. To address this issue, we decouple the network structure from the node signals and propose a two-stage solution: initially, the basic self-sales and network structure prediction are conducted separately, followed by the synthesis of the propagation scale. |
Zhe Wang; Yaming Yang; Ziyu Guan; Bin Tong; Rui Wang; Wei Zhao; Hongbo Deng; |
| 479 | Context-Aware Fine-Grained Graph RAG for Query-Focused Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: To address it, we propose Context-Aware Fine-Grained Graph RAG (FG-RAG). |
Yubin Hong; ChaoFan Li; Jingyi Zhang; Yingxia Shao; |
| 480 | D3-TR: Data-driven Daily Delivery Task Rescheduling for Cost-effective Last-mile Delivery Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: (ii) Efficiency-oriented task assignment methods may lead to unfair workload among the couriers. To address the above two limitations, in this paper, we propose D3-TR, a data-driven method for task reassignment among present couriers. |
Lidi Zhang; Yinfeng Xiang; Wenjun Lyu; Zhiqing Hong; Haotian Wang; Desheng Zhang; Yunhuai Liu; Tian He; |
| 481 | LCHGNN: Towards Distributed Hypergraph Neural Network Training Based on Communication Graphs with Lightweight Communication Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, training HGNNs on large hypergraphs is limited by computational and memory bottlenecks on a single machine. To overcome this, we propose LCHGNN, a distributed training method based on a new data structure called the communication graph, which simplifies hypergraph communication by representing cut hyperedges as vertices for structured message passing. |
Taibo Wang; Yu Gu; Xinning Cui; Zhen Song; Xiaohua Li; Fangfang Li; |
| 482 | ESPRESSO: Privacy-Preserving Keyword Search on Decentralized Data with Differential Visibility Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present ESPRESSO, a system designed for scalable and privacy-preserving keyword search in decentralized data cooperatives. |
Mohamed Ragab; Mohammad Bahrani; Helen Oliver; Thanassis Tiropanis; Alexandra Poulovassilis; Adriane Chapman; George Roussos; |
| 483 | Sarcasm Subtype-Specific Reasoning in Dialogue with Multimodal Cues Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Sarcasm can be further categorized into specific subtypes based on the forms of inversion it employs, such as nonverbal cues, dialogue context, and exaggerated word emphasis. To address this, we introduce a novel task called Sarcasm Subtype-specific Reasoning Generation (SSRG). |
Choongwon Kang; Wonbyung Lee; Seunghyun Hwang; Sunho Tae; Seungjong Sun; Jang Hyun Kim; |
| 484 | EnhanceMyPrompt: Rewriting Chat Queries for Effective Response Generation from LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We propose EnhanceMyPrompt, which uses small language models (SLMs) to enrich prompts by adding sub-intents/constraints, suggesting placeholders, and recommending popular values. |
Tushar Abhishek; Manas Jain; Shishir Hardia; Shreevignesh Suriyanarayanan; Sandra Anil; Rushabh Gandhi; Manish Gupta; |
| 485 | Learnable Orthogonal Decomposition for Non-Regressive Prediction for PDE Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose Learnable Orthogonal Decomposition (LOD), a non-regressive framework that integrates ideas from classical Proper Orthogonal Decomposition (POD) with modern deep learn- ing. |
Yun Young Choi; Kyujin Han; Joohwan Ko; Sangwook Baek; Seunghwan Lee; |
| 486 | VQA-Induct: Instruction Induction for Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, current approaches for enhancing VQA reasoning performance often assume access to extensive resources such as large annotated datasets, external tools, or numerous demonstrations, which are impractical for real-world users who typically possess only a few demonstrations. We present VQA-Induct, a framework for data-scarce scenarios that leverages MLLMs’ instruction induction capabilities to induce reusable, purely textual task-level instructions from as few as three demonstrations of the same task, then applies these instructions to new instances using only their image-question pairs. |
Po-Chun Chen; Hen-Hsen Huang; Hsin-Hsi Chen; |
| 487 | MRCLQR: A Framework for Logical Query Reasoning Based on Multi-information Relation Constraints Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Although type annotations provide semantic priors for entities, their coarse-grained features cannot comprehensively characterize entity attributes; conversely, relational structure can enhance semantic representation, but the incompleteness of edges in real-world graphs limits modeling when relying on a single information source. To address these issues, we propose MRCLQR (Multi-information Relation Constraint-based Logical Query Reasoning), a framework with three core innovations: (1) an Information Semantic Alignment module based on contrastive learning, which achieves cross-modal semantic collaboration via entity-type-structure pairing; (2) a Constraint-aware Relation Encoding method that decomposes relation semantics into domain aggregation features, relation ontology semantics, and range constraint features; and (3) Neural-Symbolic Operators guided by domain constraints, which narrow the reasoning space through a constraint-aware attention mechanism. |
Pengwei Pan; Yu Liu; Jun Ma; Jianfeng Qu; Wen Hua; Yanmei Kang; |
| 488 | On Verifiable Legal Reasoning: A Multi-Agent Framework with Formalized Knowledge Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces a modular multi-agent framework that decomposes legal reasoning into distinct knowledge acquisition and application stages. |
Albert Sadowski; Jaroslaw A. Chudziak; |
| 489 | The ReQAP System for Question Answering Over Personal Information Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This works presents the ReQAP system that supports users with answers for complex questions that involve filters, joins and aggregation over heterogeneous sources. |
Philipp Christmann; Gerhard Weikum; |
| 490 | Adaptive Spline Networks in The Kolmogorov-Arnold Framework: Knot Analysis and Stability Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we analyze KANs through the lens of spline knot behavior and derive lower and upper bounds on the number of knots in B-spline-based KANs. |
Liangwei Nathan Zheng; Wei Emma Zhang; Lin Yue; Miao Xu; Olaf Maennel; Weitong Chen; |
| 491 | Enhancing Information Diffusion Prediction Via Multiple Granularity Hypergraphs and Position-aware Sequence Model Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, they typically focus on a single temporal scale and lack the ability to effectively model temporal influence, which limits their performance in diffusion prediction tasks. To address these limitations, we propose a novel method (MHPS) to enhance information diffusion prediction via multiple granularity hypergraphs and a position-aware sequence model. |
Weikai Jing; Yuchen Wang; Haotong Du; Songxin Wang; Xiaoyu Li; Chao Gao; |
| 492 | MU-OT: Effective and Unified Machine Unlearning with Optimal Transport for Feature Realignment Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose a novel efficient unlearning framework based on Optimal Transport, which can effectively work on both class-wise and instance-wise unlearning tasks. |
Sangjun Chung; Simon S. Woo; |
| 493 | FASE: Feature-Aligned Scene Encoding for Open-Vocabulary Object Detection in Remote Sensing Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, existing methods approximate scene context by simply averaging the text embeddings of the image’s object labels, which is insufficient to capture the rich linguistic context present in RS scenes. To address this limitation, we propose a novel Feature-Aligned Scene Encoding (FASE), which constructs comprehensive scene representations through high-quality captions generated by a specialized vision-language model. |
Hyeonsu Hwang; Simon S. Woo; |
| 494 | SpeedSteiner: A Fast O(k1/2)-Approximation Algorithm for Directed Steiner Tree Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: However, to date, there are no efficient algorithms with quality guarantees. In this paper, we take on this challenge and offer a fast algorithm with provable approximation guarantees. |
Guangyi Zhang; Nikolaj Tatti; Aristides Gionis; |
| 495 | Dual Denoising Diffusion Model for Session-based Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we explore a novel direction by introducing diffusion models for denoising in SSR. |
Mengying Lu; Hai-Tao Zheng; Lan Zhou; Qi Li; Jinxiao Shan; Zhixing Li; Hong-Gee Kim; |
| 496 | LLM-as-a-Judge in Entity Retrieval: Assessing Explicit and Implicit Relevance Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this work, we evaluate LLM-based judgments against two complementary supervision signals: human-annotated relevance labels from the DBpedia-Entity benchmark and implicit feedback from user clicks in the LaQuE dataset. |
Mohammad Hossein Saliminabi; Negar Arabzadeh; Seyed Mohammad Hosseini; Dimitrios Androutsos; Morteza Zihayat; Ebrahim Bagheri; |
| 497 | Datasets for Supervised Adversarial Attacks on Neural Rankers Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We introduce a novel dataset for adversarial rank attacks against neural rankers, enabling systematic research on robustness. |
Amir Khosrojerdi; Amin Bigdeli; Radin Hamidi Rad; Morteza Zihayat; Charles L. A. Clarke; Ebrahim Bagheri; |
| 498 | PRECISE: Pre-training and Fine-tuning Sequential Recommenders with Collaborative and Semantic Information Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: Precise employs a pre-training framework that models users’ comprehensive interests across all recommendation scenarios combining collaborative signals with semantic information. |
Chonggang Song; Chunxu Shen; Hao Gu; Yaoming Wu; Lingling Yi; Jie Wen; Chuan Chen; |
| 499 | GRIT: An Accurate and Efficient Graph Stream Summarization for Temporal Query Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose GRIT, an accurate and efficient Graph stReam summarIzation for Temporal query. |
Jingxian Hu; Guozhang Sun; Xin Wang; Yuhai Zhao; Yuan Li; Xingwei Wang; |
| 500 | Discovering Group Collapser for Network Resilience Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: In this paper, we propose and study the collapsed follower maximization problem, aiming to maximize the number of coreness-decreased vertices by finding a group collapser (collapsing a set of vertices) with a given budget. |
Guozhang Sun; Haoyuan Wang; Yuhai Zhao; Zhengkui Wang; Yuan Li; Xingwei Wang; |
This table only includes 500 papers selected by our daily digest algorithm. To browse the full list, please visit Paper Digest: CIKM-2025 (Full List).