Paper Digest: SIGIR 2025 Papers & Highlights
SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval) is one of the top information retrieval conferences in the world. 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.
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TABLE 1: Paper Digest: SIGIR 2025 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These methods, however, often separate the contrastive retrieval task from the distillation process, treating it as an independent loss term. To overcome these limitations, we introduce DiSCo (Distillation of Sparse Conversational retrieval), a novel approach that unifies retrieval and context modeling through a relaxed distillation objective. |
Simon Lupart; Mohammad Aliannejadi; Evangelos Kanoulas; |
| 2 | Clarifying Ambiguities: on The Role of Ambiguity Types in Prompting Methods for Clarification Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on the concept of ambiguity for clarification, seeking to model and integrate ambiguities in the clarification process. |
Anfu Tang; Laure Soulier; Vincent Guigue; |
| 3 | Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current methods tend to model these two types of information mixedly, leading to misinterpretation of users’ actual needs, thereby lowering the accuracy of recommendations. To address this issue, this paper proposes a novel model to introduce contextual disentanglement for improving conversational recommender systems, named DisenCRS. |
Guojia An; Jie Zou; Jiwei Wei; Chaoning Zhang; Fuming Sun; Yang Yang; |
| 4 | MSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a multi-modal semantic graph prompt learning framework for CRS, named MSCRS. |
Yibiao Wei; Jie Zou; Weikang Guo; Guoqing Wang; Xing Xu; Yang Yang; |
| 5 | Action First: Leveraging Preference-Aware Actions for More Effective Decision-Making in Interactive Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (2) In the action execution stage, there is a unidirectional flow from external recommendation tools to LLMs, where these tools fail to interpret user preferences effectively, thus reducing the recommendation accuracy. To address these challenges, we introduce Action-First Interactive Recommender System(AF-IRS), a novel model that uses preference-aware actions to guide decision-making. |
Renting Rui; Yunjia Xi; Weiwen Liu; Jianghao Lin; Bo Chen; Ruiming Tang; Weinan Zhang; Yong Yu; |
| 6 | Bridging The Gap: From Ad-hoc to Proactive Search in Conversations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Conv2Query, a novel conversation-to-query framework that adapts ad-hoc retrievers to PSC by bridging the input gap between ad-hoc search and PSC. |
Chuan Meng; Francesco Tonolini; Fengran Mo; Nikolaos Aletras; Emine Yilmaz; Gabriella Kazai; |
| 7 | Search-Based Interaction For Conversation Recommendation Via Generative Reward Model Based Simulated User Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, to strike a balance between effectiveness and efficiency, we draw inspiration from the paradigm of reward-guided search in complex reasoning tasks and employ beam search for the interaction process. On top of this, we propose an efficient candidate ranking method to improve the recommendation results derived from interaction. |
Xiaolei Wang; Chunxuan Xia; Junyi Li; Fanzhe Meng; Lei Huang; Jinpeng Wang; Wayne Xin Zhao; Ji-Rong Wen; |
| 8 | OmniNER2025: Diverse and Comprehensive Fine-Grained NER Dataset and Benchmark for Chinese Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To expand NER to informal and diverse Chinese text scenarios, we have proposed a new large-scale Chinese NER dataset, OmniNER2025. |
Yong Zhou; Shuaipeng Liu; Yunqing Li; Mengting Hu; Wen Dai; Xiaowei Zhao; Xiujuan Xu; |
| 9 | LLM-Assisted Relevance Assessments: When Should We Ask LLMs for Help? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, a complete replacement with LLMs is argued to be too risky and not fully reliable.In this paper, we propose LLM-Assisted Relevance Assessments (LARA), an effective method to balance manual annotations with LLM annotations, which helps to build a rich and reliable test collection even under a low budget. |
Rikiya Takehi; Ellen M. Voorhees; Tetsuya Sakai; Ian Soboroff; |
| 10 | Advancing Ship Re-Identification in The Wild: The ShipReID-2400 Benchmark Dataset and D2InterNet Baseline Method Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These challenges make it difficult to achieve ideal results by directly applying existing ReID methods. To address these challenges, in this paper, we introduce ShipReID-2400, a dataset for ship ReID compiled from a real-world intelligent waterway traffic monitoring system. |
Baolong Liu; Roukai Huang; Xin Pan; Chuanhuang Li; Jie Sun; Jianfeng Dong; Xun Wang; |
| 11 | ΜDS: Multi-Objective Data Snippet Extraction for Dataset Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While the goodness of a data snippet has been studied from various aspects, in this paper we propose to, for the first time, jointly optimize compactness, relevance, representativeness, and cohesiveness in snippet extraction. To extract such multi-objective data snippets, we formulate a new combinatorial optimization problem and design an efficient algorithm with a proved worst-case approximation ratio. |
Xiao Zhou; Qiaosheng Chen; Jiageng Chen; Gong Cheng; |
| 12 | IVCR-200K: A Large-Scale Multi-turn Dialogue Benchmark for Interactive Video Corpus Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the Interactive Video Corpus Retrieval (IVCR) task, a more realistic setting that enables multi-turn, conversational, and realistic interactions between the user and the retrieval system. |
Ning Han; Yawen Zeng; Shaohua Long; Chengqing Li; Sijie Yang; Dun Tan; Jianfeng Dong; Jingjing Chen; |
| 13 | Rethinking Continual Knowledge Graph Embedding: Benchmarks and Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite these advancements, current CKGE studies and benchmarks primarily focus on handling the increasing scale of data while overlooking changes in graph patterns. These changes, altering the graph structure of KGs, are referred to as pattern shifts in this paper. |
Tianzhe Zhao; Jiaoyan Chen; Yanchi Ru; Qika Lin; Yuxia Geng; Haiping Zhu; Yudai Pan; Jun Liu; |
| 14 | Towards Better Evaluating Multi-Query Sessions: A Measure Based on The Theory of Planned Behavior Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Within the TPB framework, we propose sTPB, a new measure that adapts to users’ expectation management modes by considering users’ expectations of gains and costs. |
Wenbo Zhang; Fan Zhang; Jia Chen; Wei Lu; |
| 15 | The Viability of Crowdsourcing for RAG Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: How good are humans at writing and judging responses in retrieval-augmented generation (RAG) scenarios? To answer this question, we investigate the efficacy of crowdsourcing for RAG through two complementary studies: response writing and response utility judgment. |
Lukas Gienapp; Tim Hagen; Maik Fr\{o}be; Matthias Hagen; Benno Stein; Martin Potthast; Harrisen Scells; |
| 16 | A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel methodology that defines essential characteristics of the chunking process at three levels: intrinsic passage properties, extrinsic passage properties, and passages-document coherence. |
Henrik Br\r{a}dland; Morten Goodwin; Per-Arne Andersen; Alexander S. Nossum; Aditya Gupta; |
| 17 | The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our efforts focus on ”refactoring” this methodology, where we describe the AutoNuggetizer framework that specifically applies LLMs to both automatically create nuggets and automatically assign nuggets to system answers. |
Ronak Pradeep; Nandan Thakur; Shivani Upadhyay; Daniel Campos; Nick Craswell; Ian Soboroff; Hoa Trang Dang; Jimmy Lin; |
| 18 | Preference-Strength-Aware Self-Improving Alignment with Generative Preference Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods encounter two major challenges: (1) LLM-as-a-judge often produces error-prone evaluations, resulting in low-quality preference annotation, and (2) their optimization strategies often overlook the strength of preferences within binary pairs, leading to overfitting. This paper proposes a novel method, Preference-Strength-aware Optimization (PSO), to address these issues. |
Yuanzhao Zhai; Zhuo Zhang; Cheng Yang; Kele Xu; Yue Yu; Wei Li; Hui Wang; Zenglin Xu; Dawei Feng; Bo Ding; Huaimin Wang; |
| 19 | LLM-Empowered Creator Simulation for Long-Term Evaluation of Recommender Systems Under Information Asymmetry Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing RS simulators often fail to consider such a condition, making the long-term RS evaluation inaccurate. To bridge this gap, we propose a Large Language Model (LLM)-empowered creator simulation agent named CreAgent. |
Xiaopeng Ye; Chen Xu; Zhongxiang Sun; Jun Xu; Gang Wang; Zhenhua Dong; Ji-Rong Wen; |
| 20 | FashionDPO:Fine-tune Fashion Outfit Generation Model Using Direct Preference Optimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, they have limitations in terms of lacking diversity and relying on the supervised learning paradigm. Recognizing this gap, we propose a novel framework FashionDPO, which fine-tunes the fashion outfit generation model using direct preference optimization. |
Mingzhe Yu; Yunshan Ma; Lei Wu; Changshuo Wang; Xue Li; Lei Meng; |
| 21 | Open-World Fine-Grained Fashion Retrieval with LLM-based Commonsense Knowledge Infusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, to comprehend unseen attributes, we propose a novel LLM-based Commonsense Knowledge Infusion (CoKi) framework that integrates commonsense knowledge as complementary context into attribute representations using a Large Language Model (LLM). |
Jianfeng Dong; Junwei Zhu; Daizong Liu; Xiaoye Qu; Cuizhu Bao; Zhike Han; Jixiang Zhu; Xun Wang; |
| 22 | Adaptive Structure Learning with Partial Parameter Sharing for Post-Click Conversion Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce a novel principled adaptive structure learning approach, named Adap-SL, to adaptively learn the optimal network structure, adjust the number of activated (non-zero) parameters, and determine which knowledge needs to be transferred between the prediction model and the imputation model. |
Chunyuan Zheng; Hang Pan; Yang Zhang; Haoxuan Li; |
| 23 | Generative Auto-Bidding with Value-Guided Explorations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Additionally, as offline training methods are increasingly adopted to facilitate the deployment and maintenance of stable online strategies, the issues of documented behavioral patterns and behavioral collapse resulting from training on fixed offline datasets become increasingly significant. To address these limitations, this paper introduces a novel offline Generative Auto-bidding framework with Value-Guided Explorations (GAVE). |
Jingtong Gao; Yewen Li; Shuai Mao; Peng Jiang; Nan Jiang; Yejing Wang; Qingpeng Cai; Fei Pan; Peng Jiang; Kun Gai; Bo An; Xiangyu Zhao; |
| 24 | AdSight: Scalable and Accurate Quantification of User Attention in Multi-Slot Sponsored Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce AdSight, a method leveraging mouse cursor trajectories to quantify in a scalable and accurate manner user attention in multi-slot environments like SERPs. |
Mairo Villaiz\'{a}n-Vallelado; Matteo Salvatori; Kayhan Latifzadeh; Antonio Penta; Luis A. Leiva; Ioannis Arapakis; |
| 25 | ULP: Unlabeled Location Prediction from Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to construct an end-to-end fine-grained location prediction model to accurately predict the unlabeled locations mentioned in texts. |
Xi He; Yilin Liu; Yijie Sun; Xin Xing; Xingyu Lu; Yanbing Liu; |
| 26 | Towards Accurate Social User Geolocation: Mean Shift, Incremental Learning and Graph Convolutional Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel social user geolocation method (MILGCN) that innovatively integrates Mean Shift Clustering, Incremental Learning, and Graph Convolutional Networks. |
Yaqiong Qiao; Aobo Jiao; Xiangyang Luo; Chenliang Li; Jiangtao Ma; Chenkai Guo; |
| 27 | Are Generative AI Agents Effective Personalized Financial Advisors? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper investigates the effectiveness of LLM-advisors in the finance domain, focusing on three distinct challenges: (1) eliciting user preferences when users themselves may be unsure of their needs (2) providing personalized guidance for diverse investment preferences, and (3) leveraging advisor personality to build relationships and foster trust. |
Takehiro Takayanagi; Kiyoshi Izumi; Javier Sanz-Cruzado; Richard McCreadie; Iadh Ounis; |
| 28 | Brain Image Reconstruction with Retrieval-Augmented Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These signals are less precise than fMRI, which presents greater challenges for reconstruction. To address this problem, we propose BReAD (Brain Image Reconstruction with Retrieval-Augmented Diffusion), a novel framework that combines EEG/MEG signals with retrieval-augmented diffusion models to improve image reconstruction quality. |
Shuqi Zhu; Ziyi Ye; Yi Zhong; Qingyao Ai; Yujia Zhou; Yiqun Liu; |
| 29 | LIGHT: Enhancing Learning Path Recommendation Via Knowledge Topology-Aware Sequence Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose LIGHT, a knowLedge topology-aware sequence optImization model for enhancing learninG patH recommendaTion. |
Xiaoshan Yu; Shangshang Yang; Ziwen Wang; Siyu Song; Haiping Ma; Zhiguang Cao; Xingyi Zhang; |
| 30 | Interpretable Knowledge Tracing with Difficulty-Aware Attention and Selective State Space Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models typically adopt simpler structures to reduce complexity and avoid overfitting, which limits their ability to effectively capture the sequential characteristics of learning compared to sequence-based methods. To address these limitations, this paper aims to integrate the strengths of both types of methods by proposing an Interpretable KT approach with Difficulty-Aware Attention and Selective State Space Model (ASIKT). |
Yang Qin; Xinning Zhu; Xiaosheng Tang; Chunhong Zhang; Kunbao Wu; Fengjie Chang; Jianzhou Diao; Zheng Hu; |
| 31 | A Knowledge Extraction Framework on Cyber Threat Reports with Enhanced Security Profiles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, domain-specific definitions/forms of the relation types and knowledge representations are also crucial for effective utilization of knowledge. In this paper, we propose a novel knowledge extraction framework on CTRs to address the above concerns. |
Yongxin Cai; Jing Qiu; Fan Zhang; Qiang Li; Lei Chen; |
| 32 | Enhancing The Patent Matching Capability of Large Language Models Via The Memory Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose MemGraph, a method that augments the patent matching capabilities of LLMs by incorporating a memory graph derived from their parametric memory. |
Qiushi Xiong; Zhipeng Xu; Zhenghao Liu; Mengjia Wang; Zulong Chen; Yue Sun; Yu Gu; Xiaohua Li; Ge Yu; |
| 33 | MIDI-Zero: A MIDI-driven Self-Supervised Learning Approach for Music Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MIDI-Zero, a novel self-supervisedlearning framework for CBMR that operates entirely on MIDI representations. |
Yuhang Su; Wei Hu; Hongfeng Gao; Fan Zhang; |
| 34 | How Cohesive Are Community Search Results on Online Social Networks?: An Experimental Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study provides new insights that could guide the development of future community search methods. |
Yining Zhao; Sourav S Bhowmick; Nastassja L. Fischer; SH Annabel Chen; |
| 35 | Mitigating Source Bias with LLM Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce LLM-SBM, a novel LLM alignment framework for source bias mitigation. |
Sunhao Dai; Yuqi Zhou; Liang Pang; Zhuoyang Li; Zhaocheng Du; Gang Wang; Jun Xu; |
| 36 | RankingSHAP – Faithful Listwise Feature Attribution Explanations for Ranking Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing attribution methods typically provide pointwise explanations, focusing on why a single document received a high-ranking score, rather than considering the relationships between documents in a ranked list. We present three key contributions to address this gap. |
Maria Heuss; Maarten de Rijke; Avishek Anand; |
| 37 | BotBR: Social Bot Detection with Balanced Feature Fusion and Reliability-Enhanced Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing approaches face two key challenges: imbalanced feature fusion across different modalities and edge heterophily, which limit their effectiveness. To address these issues, we propose BotBR, a novel bot detection framework. |
Qilong Lin; Jingya Zhou; |
| 38 | PATFinger: Prompt-Adapted Transferable Fingerprinting Against Unauthorized Multimodal Dataset Usage Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Intrusive methods can adapt to multimodal datasets but degrade model accuracy, while non-intrusive methods rely on label-driven decision boundaries that fail to guarantee stable behaviors for verification. To address these issues, we propose a novel prompt-adapted transferable fingerprinting scheme from a training-free perspective, called PATFinger, which incorporates the global optimal perturbation (GOP) and the adaptive prompts to capture dataset-specific distribution characteristics. |
Wenyi Zhang; Ju Jia; Xiaojun Jia; Yihao Huang; Xinfeng Li; Cong Wu; Lina Wang; |
| 39 | Document Screenshot Retrievers Are Vulnerable to Pixel Poisoning Attacks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose three pixel poisoning attack methods designed to compromise VLM-based retrievers and evaluate their effectiveness under various attack settings and parameter configurations. |
Shengyao Zhuang; Ekaterina Khramtsova; Xueguang Ma; Bevan Koopman; Jimmy Lin; Guido Zuccon; |
| 40 | Joint Item Embedding Dual-view Exploration and Adaptive Local-Global Fusion for Federated Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The latter arises from the lack of modeling the relative importance of local and global contributions to personalized user preferences. To address the above challenges, we propose FedIAR which contains two modules, i.e., item embedding dual-view exploration and adaptive local-global fusion. |
Pengyang Zhou; Chaochao Chen; Weiming Liu; Wenkai Shen; Xinting Liao; Huarong Deng; Zhihui Fu; Jun Wang; Wu Wen; Xiaolin Zheng; |
| 41 | LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we develop a simulation pipeline and a dataset with ~56k generated news of diverse types to investigate the effects of LLM-generated fake news within neural news recommendation systems. |
Beizhe Hu; Qiang Sheng; Juan Cao; Yang Li; Danding Wang; |
| 42 | The Magnitude of Truth: On Using Magnitude Estimation for Truthfulness Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conduct a crowdsourcing study by collecting assessments on claims sourced from the PolitiFact fact-checking organization using ME. |
Michael Soprano; Denis Eduard Tapu; David La Barbera; Kevin Roitero; Stefano Mizzaro; |
| 43 | Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study examines the efficiency and effectiveness of crowdsourced truthfulness assessments through a comparative analysis of two approaches: one involving full-length webpages as evidence for each claim, and another using summaries for each evidence document generated with an LLM. |
Kevin Roitero; Dustin Wright; Michael Soprano; Isabelle Augenstein; Stefano Mizzaro; |
| 44 | Collaboration and Controversy Among Experts: Rumor Early Detection By Tuning A Comment Generator Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This inspires us to address the RED issue by generating more human-like comments to support this hypothesis. To implement this idea, we tune a comment generator by simulating expert collaboration and controversy and propose a new RED framework named CAMERED. |
Bing Wang; Bingrui Zhao; Ximing Li; Changchun Li; Wanfu Gao; Shengsheng Wang; |
| 45 | Continual Origin Tracing of LLM-Generated Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a new task, continual origin tracing of LLM-generated text, which frames origin tracing in a continual learning or, more precisely, class-incremental learning manner, where new LLMs continuously emerge, and a model incrementally learns to identify new LLMs without forgetting old ones. |
Haoran Li; Quan Wang; |
| 46 | Bridging Interests and Truth: Towards Mitigating Fake News with Personalized and Truthful Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional methods, which often rely on classifiers to filter out fake content, are limited by their accuracy and their inability to fully capture the diverse interests of users. To address these challenges, we proposed PRISM — Protection-enhanced Recommendation with Interest-aware Sequential Modeling — a novel framework based on diffusion models. |
Zihan Ma; Minnan Luo; Yiran Hao; Zhi Zeng; Xiangzheng Kong; Jiahao Wang; |
| 47 | The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the saying that ”truth becomes clearer through debate,” our study introduces a novel multi-agent system with LLMs named TruEDebate (TED) to enhance the interpretability and effectiveness of fake news detection. |
Yuhan Liu; Yuxuan Liu; Xiaoqing Zhang; Xiuying Chen; Rui Yan; |
| 48 | Query Smarter, Trust Better? Exploring Search Behaviours for Verifying News Accuracy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study highlights the importance of query strategies in evaluating news and proposes that interface design can play a key role in promoting more effective search practices, serving as one component of a broader set of interventions to combat misinformation. |
David Elsweiler; Samy Ateia; Markus Bink; Gregor Donabauer; Marcos Fern\'{a}ndez Pichel; Alexander Frummet; Udo Kruschwitz; David E. Losada; Bernd Ludwig; Selina Meyer; Noel Pascual Presa; |
| 49 | Measuring Text-Image Retrieval Fairness with Synthetic Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study social bias in cross-modal text-image retrieval systems, focusing on the interaction between textual queries and image responses. |
Lluis Gomez; |
| 50 | Understanding Accuracy-Fairness Trade-offs in Re-ranking Through Elasticity in Economics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the Elastic Fairness Curve (EF-Curve) as an evaluation framework. |
Chen Xu; Jujia Zhao; Wenjie Wang; Liang Pang; Jun Xu; Tat-Seng Chua; Maarten de Rijke; |
| 51 | Fairness-Aware Classification Over Incomplete Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Transformer-based fairness-aware prediction model FATE that mitigates the bias introduced by missing values to achieve algorithmic fairness without imputation. |
Xiaoye Miao; Lei Qiang; Guilin Huang; Yangyang Wu; Jianwei Yin; |
| 52 | Is Having Rationales Enough? Rethinking Knowledge Enhancement for Multimodal Hateful Meme Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These challenges hinder model comprehension, leading to reduced accuracy and explainability. To address these challenges, we propose a Multimodal Multi-agent Knowledge Enhanced (M2KE) framework for hateful meme detection. |
Junyu Lu; Bo Xu; Xiaokun Zhang; Haohao Zhu; Kaichun Wang; Liang Yang; Hongfei Lin; |
| 53 | Can LLMs Enhance Fairness in Recommendation Systems? A Data Augmentation Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new exploration of fairness-aware RS by prompting LLMs with the user’s personalized fairness degrees to augment fair user-item interaction for training. |
Hanzhe Li; Dazhong Shen; Chao Wang; Yuting Liu; Jingjing Gu; |
| 54 | Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although it seems appealing to directly apply the existing multi-modal fusion methods to click-through rate prediction models, these methods (1) fail to effectively disentangle commonalities and specificities across different modalities; (2) fail to consider the synergistic effects between modalities and model the complex interactions between modalities. To address the above issues, this paper proposes the Diffusion-based Multi-modal Synergy Interest Network (Diff-MSIN) framework for click-through prediction. |
Xiaoxi Cui; Weihai Lu; Yu Tong; Yiheng Li; Zhejun Zhao; |
| 55 | A Pattern-Driven Information Diffusion Prediction Model Based on Multisource Resonance and Cognitive Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While existing diffusion prediction models have effectively leveraged network structure, they often lack a deep understanding of the intrinsic patterns governing information dissemination, thus limiting their predictive power. To address this critical gap, we introduce PMRCA, a novel information diffusion prediction model inspired by social psychology and driven by fundamental propagation patterns. |
Weikang He; Yunpeng Xiao; Mengyang Huang; Xuemei Mou; Rong Wang; Qian Li; |
| 56 | Information Retrieval in The Age of Generative AI: The RGB Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools. |
Michele Garetto; Alessandro Cornacchia; Franco Galante; Emilio Leonardi; Alessandro Nordio; Alberto Tarable; |
| 57 | Towards Brain Passage Retrieval: An Investigation of EEG Query Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current approaches attempting to decode explicit text queries from brain signals have shown limited effectiveness in learning robust brain-to-text representations, often failing to capture the nuanced semantic information present in brain patterns. To address these limitations, we propose BPR (Brain Passage Retrieval), a novel framework that eliminates the need for intermediate query translation by enabling direct retrieval of relevant passages from users’ brain signals. |
Niall Mcguire; Yashar Moshfeghi; |
| 58 | Designing Search Engine Result Pages for Immersive Virtual Reality: Insights from Eye-Tracking and User Perception Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work explores how different information arrangements in 3D virtual space impact users’ search behaviors and preferences.This paper presents the results of an exploratory within-subjects user study that investigated search behaviors, perceptions, and eye-tracking behaviors for four different spatial arrangements of search results (”list” – 2D list; ”curve3” – 3×3 curved grid; ”curve4” – 4×4 curved grid; and ”sphere” – 4×4 semi-spherical grid) in a virtual reality across two different task types (Find All relevant, Pick 3 best). |
Austin R. Ward; Bogeum Choi; Robert Capra; |
| 59 | How Users Interact with Generative Information Retrieval Systems: A Study of User Behavior and Search Experience Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, systematic investigations into user behavior and search experience on generative IR systems are notably lacking. To address this gap, we conducted a user study using Bing Chat to explore user behavior and feedback on generative IR systems. |
Yidong Liang; Zhijing Wu; Fan Zhang; Dandan Song; Heyan Huang; |
| 60 | Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enhance robustness, we introduce an active learning approach that prioritizes challenging user personas during training. |
Tao He; Lizi Liao; Ming Liu; Bing Qin; |
| 61 | PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models Via Bilevel Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent efforts have focused on the security of RAG-based LLMs, yet existing attack methods face three critical challenges: (1) their effectiveness declines sharply when only a limited number of poisoned texts can be injected into the knowledge database (2) they lack sufficient stealth, as the attacks are often detectable by anomaly detection systems, which compromises their effectiveness, and (3) they rely on heuristic approaches to generate poisoned texts, lacking formal optimization frameworks and theoretic guarantees, which limits their effectiveness and applicability. To address these issues, we propose coordinated Prompt-RAG attack (PR-attack), a novel optimization-driven attack that introduces a small number of poisoned texts into the knowledge database while embedding a backdoor trigger within the prompt. |
Yang Jiao; Xiaodong Wang; Kai Yang; |
| 62 | Reverse-Engineering The Retrieval Process in GenIR Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, in contrast to established retrieval architectures like cross-encoders or bi-encoders, their internal computations remain largely unknown. In this work, we investigate this retrieval mechanism and uncover the roles played by different model components (self-attention, cross-attention, MLPs) and their interaction to generate the document identifier. |
Anja Reusch; Yonatan Belinkov; |
| 63 | CG-RAG: Research Question Answering By Citation Graph Retrieval-Augmented LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Contextualized Graph Retrieval-Augmented Generation (CG-RAG), a novel framework that integrates sparse and dense retrieval signals within graph structures to enhance retrieval efficiency and subsequently improve generation quality for research question answering. |
Yuntong Hu; Zhihan Lei; Zhongjie Dai; Allen Zhang; Abhinav Angirekula; Zheng Zhang; Liang Zhao; |
| 64 | Heterogeneous Graph Embedding Made More Practical Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these GNN-based approaches are computationally intensive due to the substantial parameter training involved. To address these challenges, we propose HGSketch, a practical heterogeneous graph embedding algorithm that balances performance and temporal efficiency without the powerful workhorses. |
Fangfang Li; Huihui Zhang; Wei Li; Wei Wu; |
| 65 | Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite encouraging results, this approach either requires numerous queries to LLMs or suffers from reduced performance due to noisy labels generated by LLMs. To address these challenges, we introduce Locle, an active self-training framework that does Label-free nOde Classification with LLMs cost-Effectively. |
Taiyan Zhang; Renchi Yang; Yurui Lai; Mingyu Yan; Xiaochun Ye; Dongrui Fan; |
| 66 | Enhancing Homophily in Heterogeneous Graph Contrastive Learning Via Connection Strength and Multi-view Self-Expression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge this research gap, we proposed a novel heterogeneous graph contrastive learning framework, termed HGMS, which leverages connection strength and multi-view self-expression to learn homophilous node representations. |
Haosen Wang; Chenglong Shi; Can Xu; Surong Yan; Rong Xie; Pan Tang; |
| 67 | InfoNCE Is A Free Lunch for Semantically Guided Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that GCL is essentially a Positive-Unlabeled (PU) learning problem, where the definition of self-supervised tasks should be semantically guided, i.e., augmented samples with similar semantics are considered positive, while others, with unknown semantics, are treated as unlabeled. |
Zixu Wang; Bingbing Xu; Yige Yuan; Huawei Shen; Xueqi Cheng; |
| 68 | Generative Recommender with End-to-End Learnable Item Tokenization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Current approaches often treat item tokenization and generative recommendation training as separate processes, which can lead to suboptimal performance. To overcome this issue, we introduce ETEGRec, a novel End-To-End Generative Recommender that unifies item tokenization and generative recommendation into a cohesive framework. |
Enze Liu; Bowen Zheng; Cheng Ling; Lantao Hu; Han Li; Wayne Xin Zhao; |
| 69 | Dynamic Time-aware Continual User Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a practical evaluation scenario on which CL-based universal user representation learning approaches should be evaluated, which takes into account the passage of time as tasks progress. |
Seungyoon Choi; Sein Kim; Hongseok Kang; Wonjoong Kim; Chanyoung Park; |
| 70 | MINTT: Memory Inductive Transfer for Temporal Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, transferring the TGNN model from one dataset to another is not trivial, as it contains node-specific memory modules vital for performance, resulting in them being inherently non-transferable. To overcome this limitation, we propose a novel transfer method that effectively utilizes common attributes between source and target datasets by decoupling graph nodes and corresponding attributes via bipartite encoding. |
Tanishq Dubey; Sidharth Agarwal; Shubham Gupta; Srikanta Bedathur; |
| 71 | A Generalised and Adaptable Reinforcement Learning Stopping Method Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a Technology Assisted Review (TAR) stopping approach based on Reinforcement Learning (RL). |
Reem Bin-Hezam; Mark Stevenson; |
| 72 | SAFT: Structure-aware Transformers for Textual Interaction Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose SAFT, a new architecture that integrates language- and graph-based modules for the effective fusion of textual and structural semantics in the representation learning of interactions. |
Hongtao Wang; Renchi Yang; Hewen Wang; Haoran Zheng; Jianliang Xu; |
| 73 | HCDS: Hierarchical Clustering for Cold-Start Few-Shot Data Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They ignore the entire representativeness of samples within a cluster. To tackle these challenges, we propose a novel framework HCDS : Hierarchical Clustering for Cold-Start Few-Shot Data Selection. |
Yuhua Zhao; Zhixin Han; Xunzhi Wang; Bitong Luo; Hang Gao; Minlie Huang; Mengting Hu; |
| 74 | Beyond General Alignment: Fine-Grained Entity-Centric Image-Text Matching with Multimodal Attentive Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we work towards Entity-centric Image-Text Matching (EITM), a finer-grained image-text matching task that aligns texts and images centered around specific entities. |
Yaxiong Wang; Lianwei Wu; Lechao Cheng; Zhun Zhong; Yujiao Wu; Meng Wang; |
| 75 | FiRE: Enhancing MLLMs with Fine-Grained Context Learning for Complex Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, pioneering studies, while promising, overlook the potential of fine-grained context modeling and disentangled fine-tuning objectives in enhancing MLLMs’ retrieval performance, particularly for complex tasks such as long-text-to-image retrieval, visual dialog retrieval, and composed image retrieval (CIR). Therefore, in this work, we propose an automated fine-grained multimodal quintuple dataset construction pipeline and a novel two-stage fine-grained multimodal fine-tuning strategy. |
Bohan Hou; Haoqiang Lin; Xuemeng Song; Haokun Wen; Meng Liu; Yupeng Hu; Xiangyu Zhao; |
| 76 | Revolutionizing Text-to-Image Retrieval As Autoregressive Token-to-Voken Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we aim to enhance both effectiveness and efficiency by transforming the text-to-image retrieval task into a token-to-voken generation problem, where fine-grained interactions are incorporated to improve effectiveness while maintaining high efficiency. |
Yongqi Li; Hongru Cai; Wenjie Wang; Leigang Qu; Yinwei Wei; Wenjie Li; Liqiang Nie; Tat-Seng Chua; |
| 77 | Diffusion Augmented Retrieval: A Training-Free Approach to Interactive Text-to-Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we introduce Diffusion Augmented Retrieval (DAR), a framework that generates multiple intermediate representations via LLM-based dialogue refinements and DMs, producing a richer depiction of the user’s information needs. |
Zijun Long; Kangheng Liang; Gerardo Aragon Camarasa; Richard Mccreadie; Paul Henderson; |
| 78 | Rethinking Pseudo Word Learning in Zero-Shot Composed Image Retrieval: From An Object-Aware Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we rethink how to learn pseudo words based on the objects attended by the text and propose a Multi-Object Aware ZS-CIR framework (MOA). |
Zhe Li; Lei Zhang; Kun Zhang; Weidong Chen; Yongdong Zhang; Zhendong Mao; |
| 79 | Class Activation Values: Lucid and Faithful Visual Interpretations for CLIP-based Text-Image Retrievals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a fine-grained interpretation method, termed Class Activation Values (CAV), to provide lucid and faithful visual explanations for CLIP-based text-image retrievals. |
Pengxu Chen; Huazhong Liu; Jihong Ding; Xinghao Huang; Shaojun Zou; Laurence Tianruo Yang; |
| 80 | Two-Stage Adversarial Training for Deep Hashing Via Representation Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we argue that directly using the original deep hashing loss will guide the model to learn excessive non-robust patterns from clean examples when extracting discriminative semantic information, thereby limiting model robustness. To tackle this, we propose a novel Clean model Representation Distillation based Adversarial Training (CRDAT) method, which enables the robust model to learn both discriminative semantic information and robust patterns by separating these two losses into two stages, i.e., standard training stage of a clean teacher model and adversarial training stage of a robust student model. |
Fei Zhu; Huashan Chen; Wanqian Zhang; Lin Wang; Zheng Lin; Bo Li; |
| 81 | Unified Category and Style Generalization for Instance-Level Sketch Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose the Dual-Attentive Prompt (DAP) method, which unifies category generalization and style adaptation into a single, interpretable framework. |
Zechao Hu; Zhengwei Yang; Hao Li; Yixiong Zou; Fengbin Zhu; Zheng Wang; |
| 82 | Queries Are Not Alone: Clustering Text Embeddings for Video Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel framework, the Video-Text Cluster (VTC), which enhances video retrieval by clustering text queries to capture a broader semantic scope. |
Peiyang Liu; Xi Wang; Ziqiang Cui; Wei Ye; |
| 83 | Question-Answering Dense Video Events Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For improvement, we propose DeVi, a novel training-free MLLM approach that highlights a hierarchical captioning module, a temporal event memory module, and a self-consistency checking module to respectively detect, contextualize and memorize, and ground dense-events in long videos for question answering. |
Hangyu Qin; Junbin Xiao; Angela Yao; |
| 84 | AV-NAS: Audio-Visual Multi-Level Semantic Neural Architecture Search for Video Hashing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Incorporating both visual and audio signals, however, complicates neural architecture design, rendering the manual crafting of joint audio-visual neural network models challenging. To address this issue, we propose AV-NAS, a method that leverages data-driven Neural Architecture Search (NAS) within a tailored audio-visual network space to automatically discover the optimal video hashing network. |
Yong Chen; Yuxiang Zhou; Hailiang Dong; Rui Liu; Zhouchen Lin; Dell Zhang; |
| 85 | Understanding The Effect of Opinion Polarization in Short Video Browsing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, we demonstrate the potential of using EEG signals to predict users’ exposure to polarized short video content. By exploring the relationships between OP, brain signals, and user behavior, our research offers a novel perspective in understanding the dynamics of short video browsing and proposes an innovative method for quantifying the impact of OP in this context. |
Bangde Du; Ziyi Ye; Zhijing Wu; Monika Jankowska; Qingyao Ai; Yiqun Liu; |
| 86 | Gaming for Boundary: Elastic Localization for Frame-Supervised Video Moment Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This task is still in its infancy due to the following challenges: 1) indiscernible intra-modal information and 2) inflexible inter-modal information interaction. In light of these challenges, we introduce the Gaming fOr elAstic Localization (GOAL) method for frame-supervised video moment retrieval. |
Hao Liu; Yupeng Hu; Kun Wang; Yinwei Wei; Liqiang Nie; |
| 87 | ARC: Approximate Relevant Clip Query in Large-Scale Video Repositories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing solutions face two major challenges: insufficient flexibility in handling complex query conditions involving statistical reasoning and temporal constraints, and low efficiency under high query quality requirements and resource constraints.In this paper, we formally introduce the concept of relevant clip queries for the first time, providing a framework for querying relevant clips in a much more flexible way. To answer such queries efficiently, we propose the approximate query processing system ARC, which aims to maximize the recall and minimize the query overhead while ensuring the confidence of the query results. |
Yue Chen; Yinan Jing; Ziqiang Yu; Xiaohui Yu; Zhenying He; Kai Zhang; X. Sean Wang; |
| 88 | Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, these functions often adhere to fixed forms, such as piece-wise linear functions, which exhibit limited expressiveness and flexibility, thereby constraining their effectiveness in complex calibration scenarios. To mitigate this issue, we propose implementing a calibrator using an Unconstrained Monotonic Neural Network (UMNN), which can learn arbitrary monotonic functions with great modeling power. |
Yimeng Bai; Shunyu Zhang; Yang Zhang; Hu Liu; Wentian Bao; Enyun Yu; Fuli Feng; Wenwu Ou; |
| 89 | Techie: Tackling Video Prefetching at Edge Networks As POMDP Via An Intrinsically Motivated RL Agent Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we tackle video prefetching at edge networks as a Partially Observable Markov Decision Process, and propose Techie, an intrinsically motivated policy-gradient reinforcement learning agent that differentiates intrinsic rewards from extrinsic rewards based on their availability to edge networks. |
Nawras Alkassab; Chin-Tser Huang; Tania Lorido Botran; |
| 90 | DePro: Domain Ensemble Using Decoupled Prompts for Universal Cross-Domain Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this approach often struggles with the domain and semantic shifts inherent in UCDR. To overcome these limitations, we propose a novel prompt decoupling strategy that separates prompts into universal domain prompts (UDPs) and class prompts (CPs). |
Kaixiang Chen; Pengfei Fang; Hui Xue; |
| 91 | Meta-Guided Adaptive Weight Learner for Noisy Correspondence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods can ineluctably result in the inclusion of false positives, which significantly degrade the performance. To tackle this issue, we propose a novel method, named the Meta Similarity Importance Assignment Network (MSIAN), to achieve robust cross-modal retrieval. |
Chenyu Mu; Erkun Yang; Cheng Deng; |
| 92 | Generating Difficulty-aware Negative Samples Via Conditional Diffusion for Multi-modal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to Generate Difficulty-aware Negative Samples via conditional diffusion for MMRec (denoted as GDNSM). |
Wenze Ma; Chenyu Sun; Yanmin Zhu; Zhaobo Wang; Xuhao Zhao; Mengyuan Jing; Jiadi Yu; Feilong Tang; |
| 93 | Boosting Discriminability for Robust Multimodal Entity Linking with Visual Modality Missing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel setting dubbed MEL-MM to simulate the practical challenge, and reveal that the semantic discriminability is a crucial factor to enhance the anti-missingness resilience. |
Mingrui Lao; Zheng Li; Yanming Guo; Xueyi Zhang; Siqi Cai; Zhaoyun Ding; Haizhou Li; |
| 94 | Seeing Beyond Hallucinations: LLM-based Compositional Information Extraction for Multimodal Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods to mitigate these hallucinations are limited by the significant human labor required and the coarse-grained nature. To overcome these challenges, we introduce Multimodal Contrastive Decoding (MMCD), a novel decoding approach that integrates graph-structured reasoning paths with contrastive decoding. |
Xinwei Li; Li Lin; Shuai Wang; Hanqian Wu; |
| 95 | Continual Text-to-Video Retrieval with Frame Fusion and Task-Aware Routing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the first benchmark for Continual Text-to-Video Retrieval (CTVR) to address the limitations of existing approaches. |
Zecheng Zhao; Zhi Chen; Zi Huang; Shazia Sadiq; Tong Chen; |
| 96 | CLIP-AdaM: Adapting Multi-view CLIP for Open-set 3D Object Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building upon the strong open-world representation capabilities of CLIP, we introduce CLIP-AdaM, which, to our knowledge, represents the first attempt to adapt a CLIP model for open-set 3DOR with minimal effort. |
Xinwei He; Liang Ma; Yuxuan Cheng; Zhichuan Wang; Yulong Wang; Yang Zhou; Xiang Bai; |
| 97 | Multi-level Encoding with Hierarchical Alignment for Sketch-Based 3D Shape Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, conventional SBSR primarily focuses on instance-level alignment while ignoring multi-level alignment, which may neglect complex hierarchical relationships. To address these limitations, we propose a novel Multi-level Encoding with Hierarchical Alignment (MEHA) method for SBSR. |
Donglin Zhang; Changxing Li; Xiao-Jun Wu; |
| 98 | Reconciling Efficiency and Effectiveness of Exercise Retreival: An Uncertainty Reduction Hashing Approach for Computerized Adaptive Testing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose HashCAT, an efficient CAT approach based on learning to hash, aiming to balance efficiency and evaluation effectiveness. |
Haiping Ma; Weiyuan Zhou; Xiaoshan Yu; Changqian Wang; Shangshang Yang; Limiao Zhang; Xingyi Zhang; |
| 99 | Ask and Retrieve Knowledge: Towards Proactive Asking with Imperfect Information in Medical Multi-turn Dialogues Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate the collapse of models trained on synthetic data, we propose a progressive training strategy: self-reason learning by supervised fine-tuning on produced paths and knowledge alignment through direct preference optimization on doctor response.To evaluate the information gain brought by the ask action, we design a method to calculate the ask utility value (AUV) based on the expected value of perfect information (EVPI) theory. |
Bolin Zhang; Shengwei Wang; Yangqin Jiang; Dianbo Sui; Zhiying Tu; Dianhui Chu; |
| 100 | General Neural Embedding for Sequence Distance Approximation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we attempt to unify the sequence distance computation approximation from various fields and propose GnesDA. |
Zhihao Chang; Ding Wang; Xiu Tang; Kingsum Chow; Jianwei Yin; |
| 101 | ProtChatGPT: Towards Understanding Proteins with Hybrid Representation and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce ProtChatGPT, which aims to learn and understand protein structures using natural language. |
Chao Wang; Hehe Fan; Ruijie Quan; Lina Yao; Yi Yang; |
| 102 | Combining Evidence and Reasoning for Biomedical Fact-Checking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce CER (Combining Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. |
Mariano Barone; Antonio Romano; Giuseppe Riccio; Marco Postiglione; Vincenzo Moscato; |
| 103 | LLM-based Search Assistant with Holistically Guided MCTS for Intricate Information Seeking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an LLM-based search assistant that adopts a new information seeking paradigm with holistically guided Monte Carlo tree search (HG-MCTS). |
Ruiyang Ren; Yuhao Wang; Junyi Li; Jinhao Jiang; Wayne Xin Zhao; Wenjie Wang; Tat-Seng Chua; |
| 104 | OBELLA: Open The Book for Evaluating Long-Form Large Language Model Answers in Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, state-of-the-art (SOTA) automatic metrics, which are mostly supervised, remain notably less reliable than humans. In this paper, we find two key challenges behind this gap: (1) length distribution mismatch between lengthy LLM answers and shorter training answers used by current metrics; and (2) reference incompleteness, where current metrics often misjudge valid system answers absent from given references-a challenge worsened by the diversity of LLM outputs. |
Tianyu Ren; Zhaoyu Zhang; Hui Wang; Karen Rafferty; |
| 105 | Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: When deciding to read an article or incorporate it into their research, scholars often seek to quickly identify and understand its main ideas. In this paper, we aim to extract these key concepts and contributions from scientific articles in the form of Question and Answer (QA) pairs. |
Hosein Azarbonyad; Zi Long Zhu; Georgios Cheirmpos; Zubair Afzal; Vikrant Yadav; Georgios Tsatsaronis; |
| 106 | GlFoMR: A Glance-then-Focus Multimodal Reasoning Framework for Diagram Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The tight interweaving of visual and textual reasoning for MLLMs is also susceptible to hallucinations. To overcome these limitations, we propose a Glance-then-Focus Multimodal Reasoning framework named GlFoMR for DQA, which features a flexible architecture for comprehensive visual and text interaction. |
Yaxian Wang; Bifan Wei; Jun Liu; Lingling Zhang; Shuting He; Jun Li; Qika Lin; |
| 107 | An Empirical Study of Evaluating Long-form Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We collect 5,236 factoid and non-factoid long-form answers generated by different large language models and conduct a human evaluation on 2,079 of them, focusing on correctness and informativeness. |
Ning Xian; Yixing Fan; Ruqing Zhang; Maarten de Rijke; Jiafeng Guo; |
| 108 | BALI: Enhancing Biomedical Language Representations Through Knowledge Graph and Language Model Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose BALI (Biomedical Knowledge Graph and Language Model Ali gnment), a novel joint LM and KG pre-training method that augments an LM with external knowledge by the simultaneous learning of a dedicated KG encoder and aligning the representations of both the LM and the graph. |
Andrey Sakhovskiy; Elena Tutubalina; |
| 109 | Flow-guided Direct Preference Optimization for Knowledge Graph Reasoning with Trees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes FD-PORT (flow-guided direct preference optimization for knowledge graph reasoning with trees), a novel approach that combines Monte Carlo Tree Search (MCTS) with flow-guided direct preference optimization (FDPO) for KGQA tasks. |
Tiesunlong Shen; Rui Mao; Jin Wang; Xuejie Zhang; Erik Cambria; |
| 110 | Segmentation Similarity Enhanced Semantic Related Entity Fusion for Multi-modal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The segmentation of semantic data, including image segmentation and word-level descriptions, often contain implicit relationships between entities that are frequently overlooked by existing methodologies, thus limiting the effectiveness of reasoning tasks. Therefore, we propose a novel completion inference method based on fine-grained semantic segmentation, which enhances reasoning capability by utilizing implicit relationships between entities. |
Yunpeng Wang; Bo Ning; Xin Wang; Chengfei Liu; Guanyu Li; |
| 111 | Mitigating Modality Bias in Multi-modal Entity Alignment from A Causal Perspective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We believe the model can be biased toward the visual modality, leading to a shortcut image-matching task. To address this, we propose a counterfactual debiasing framework for MMEA, termed CDMEA, which investigates visual modality bias from a causal perspective. |
Taoyu Su; Jiawei Sheng; Duohe Ma; Xiaodong Li; Juwei Yue; Mengxiao Song; Yingkai Tang; Tingwen Liu; |
| 112 | From Knowledge Forgetting to Accumulation: Evolutionary Relation Path Passing for Lifelong Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, extensive entity updates to adapt to new snapshots introduce conflicts between old and new knowledge, thereby resulting in the inevitable occurrence of knowledge forgetting. To address these challenges, we propose the Evolutionary Relation Path Passing (ERPP) model for lifelong knowledge graph embedding, aiming to shift from knowledge forgetting to knowledge accumulation, thereby achieving accurate long-term prediction. |
Jing Yang; Xinfa Jiang; Xiaowen Jiang; Yuan Gao; Laurence T. Yang; Shaojun Zou; Shundong Yang; |
| 113 | AdaRPT: An Adaptive Rule Pattern Transfer Model for Fully Inductive Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This limitation severely restrict the reasoning capabilities of existing methods. In light of this, we propose the Adaptive Rule Pattern Transfer model (AdaRPT) for KGR. |
Zhiwen Xie; Zhuo Zhao; Jinjin Ma; Guangyou Zhou; Jimmy Xiangji Huang; |
| 114 | HyperG: Hypergraph-Enhanced LLMs for Structured Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Both approaches, whether relying on serialization or using SQL-like operations as an intermediary, encounter difficulties in fully capturing structural relationships and effectively handling sparse data. To address these unique characteristics of structured data, we propose HyperG, a hypergraph-based generation framework aimed at enhancing LLMs’ ability to process structured knowledge. |
Sirui Huang; Hanqian Li; Yanggan Gu; Xuming Hu; Qing Li; Guandong Xu; |
| 115 | Comprehending Knowledge Graphs with Large Language Models for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Third, existing methods struggle to capture high-order connections in the global KG. To address these limitations, we propose a novel method called CoLaKG, which leverages large language models (LLMs) to improve KG-based recommendations. |
Ziqiang Cui; Yunpeng Weng; Xing Tang; Fuyuan Lyu; Dugang Liu; Xiuqiang He; Chen Ma; |
| 116 | Parametric Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce Parametric RAG, a new RAG paradigm that integrates external knowledge directly into the feed-forward networks of an LLM through document parameterization. |
Weihang Su; Yichen Tang; Qingyao Ai; Junxi Yan; Changyue Wang; Hongning Wang; Ziyi Ye; Yujia Zhou; Yiqun Liu; |
| 117 | ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although recent approaches have explored integrating RAG with chain-of-thought reasoning or incorporating test-time search with process reward model (PRM), these methods face several untrustworthy challenges, including lack of explanations, bias in PRM training data, early-step bias in PRM scores, and ignoring post-training that fails to fully optimize reasoning potential. To address these issues, we propose Retrieval-Augmented Reasoning through Trustworthy Process Rewarding (ReARTeR), a framework that enhances RAG systems’ reasoning capabilities through both post-training and test-time scaling. |
Zhongxiang Sun; Qipeng Wang; Weijie Yu; Xiaoxue Zang; Kai Zheng; Jun Xu; Xiao Zhang; Yang Song; Han Li; |
| 118 | Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a systematic investigation of the intrinsic mechanisms by which LLMs integrate internal (parametric) and external (retrieved) knowledge in RAG scenarios. |
Yuhao Wang; Ruiyang Ren; Yucheng Wang; Wayne Xin Zhao; Jing Liu; Hua Wu; Haifeng Wang; |
| 119 | Robust Fine-tuning for Retrieval Augmented Generation Against Retrieval Defects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In real-world scenarios, imperfections in these components often lead to the retrieval of noisy, irrelevant, or misleading counterfactual information, ultimately undermining the trustworthiness of RAG systems. To address this challenge, we propose Robust Fine-Tuning (RbFT), a method designed to enhance the resilience of LLMs against retrieval defects through two targeted fine-tuning tasks. |
Yiteng Tu; Weihang Su; Yujia Zhou; Yiqun Liu; Qingyao Ai; |
| 120 | Predicting RAG Performance for Text Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present novel supervised post-retrieval prediction methods that utilize the specific characteristics of the text completion setting. |
Oz Huly; David Carmel; Oren Kurland; |
| 121 | Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the application of collaborative filtering in recommender systems, we propose a method called CFRAG, which adapts Collaborative Filtering to RAG for personalized text generation. |
Teng Shi; Jun Xu; Xiao Zhang; Xiaoxue Zang; Kai Zheng; Yang Song; Han Li; |
| 122 | Knowing You Don’t Know: Learning When to Continue Search in Multi-round RAG Through Self-Practicing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing solutions either require large amounts of expensive human-labeled process supervision data or lead to subpar performance. This paper aims to address these limitations by introducing a new framework, SIM-RAG, to explicitly enhance RAG systems’ self-awareness and multi-round retrieval capabilities. |
Diji Yang; Linda Zeng; Jinmeng Rao; Yi Zhang; |
| 123 | CIRAG: Retrieval-Augmented Language Model with Collective Intelligence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite advancements, existing RAG methods still suffer from uncertainty of prediction during the multi-round retrieval-generation process, and a lack of the ability to balance the adequacy and redundancy of retrieved information. To address these challenges, we propose CIRAG, an approach that combines the RAG process with collective intelligence. |
Chenxu Cui; Haihui Fan; Jinchao Zhang; Lin Shen; Bo Li; Weiping Wang; |
| 124 | Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While reinforcement learning-based methods, such as reinforcement learning from relevance feedback (RLRF), aim to address this misalignment through reward modeling, they introduce significant complexity, requiring the optimization of an auxiliary reward function followed by reinforcement fine-tuning, which is computationally expensive and often unstable. To address these challenges, we propose direct document relevance optimization (DDRO), which aligns token-level docid generation with document-level relevance estimation through direct optimization via pairwise ranking, eliminating the need for explicit reward modeling and reinforcement learning. |
Kidist Amde Mekonnen; Yubao Tang; Maarten de Rijke; |
| 125 | Exploring Training and Inference Scaling Laws in Generative Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel evaluation metric inspired by contrastive entropy and generation loss, providing a continuous performance signal that enables robust comparisons across diverse generative retrieval methods. |
Hongru Cai; Yongqi Li; Ruifeng Yuan; Wenjie Wang; Zhen Zhang; Wenjie Li; Tat-Seng Chua; |
| 126 | Incorporating Communication Style and Interaction of Speakers for Sarcasm Explanation in Dialogue Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While previous studies have largely focused on modeling dialogue content, they often neglect the influence of speakers and the interactions between utterances. To address this gap, we propose a novel framework called CISI, which integrates personalized communication styles, inter-speaker interaction relationships, and sarcasm-centric multimodal cues to enhance SED. |
Yuqing Li; Wenyuan Zhang; Zheng Lin; Guoxuan Ding; Weiping Wang; |
| 127 | LUSIFER: Language Universal Space Integration for Enhanced Representation in Multilingual Text Embedding Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these models predominantly focus on English, leaving multilingual embedding capabilities largely unexplored. To address this limitation, we present LUSIFER, a novel zero-shot approach that adapts LLM-based embedding models for multilingual tasks without requiring multilingual supervision. |
Hieu Man; Nghia Trung Ngo; Viet Dac Lai; Ryan A. Rossi; Franck Dernoncourt; Thien Huu Nguyen; |
| 128 | Generative Meta-Learning for Zero-Shot Relation Triplet Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods focus solely on fitting the training data during training, without specifically improving the model’s generalization performance, resulting in limited generalization capability. For this reason, we explore the integration of bi-level optimization (BLO) with pre-trained language models for learning generalized knowledge directly from the training data, and propose a generative meta-learning framework which exploits the ‘learning-to-learn’ ability of meta-learning to boost the generalization capability of generative models.Specifically, we introduce a BLO approach that simultaneously addresses data fitting and generalization. |
Wanli Li; Tieyun Qian; Yi Song; Zeyu Zhang; Jiawei Li; Zhuang Chen; Lixin Zou; |
| 129 | Reasoning and Retrieval for Complex Semi-structured Tables Via Reinforced Relational Data Transformation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce TabFormer, a framework that normalizes diverse semi-structured tables into relational data via large language models to facilitate various table retrieval and reasoning tasks. |
Haoyu Dong; Yue Hu; Yanan Cao; |
| 130 | Optimizing Tail-Head Trade-off for Extreme Multi-Label Text Classification (XMTC) with RAG-Labels and A Dynamic Two-Stage Retrieval and Fusion Pipeline Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We tackle Extreme Multi-Label Text Classification (XMTC), which involves assigning relevant labels to texts from a huge label space. Attempting to optimize the underexplored tail-head trade-off, we address the XMTC task through its core challenges of volume, skewness, and quality by proposing xCoRetriev, a novel two-stage retrieving and fusing ranking pipeline. |
Celso Fran\c{c}a; Gestefane Rabbi; Thiago Salles; Washington Cunha; Leonardo Rocha; Marcos Andr\'{e} Gon\c{c}alves; |
| 131 | CSE-SFP: Enabling Unsupervised Sentence Representation Learning Via A Single Forward Pass Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Given that state-of-the-art models in both academia and industry are predominantly based on generative architectures, there is a pressing need for an efficient unsupervised text representation framework tailored to decoder-only PLMs. To address this concern, we propose CSE-SFP, an innovative method that exploits the structural characteristics of generative models. |
Bowen Zhang; Zixin Song; Chunping Li; |
| 132 | InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify two key challenges (enlargability and transferability) in applying RAG to task planning. |
Zheng Wang; Shu Xian Teo; Jun Jie Chew; Wei Shi; |
| 133 | A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, these methods retrieve information at a coarse granularity, leading to the inclusion of irrelevant content. To address these issues, we propose a novel retrieval-based framework that integrates query selection and document ranking and shortening into a unified process. |
Shiyin Tan; Jaeeon Park; Dongyuan Li; Renhe Jiang; Manabu Okumura; |
| 134 | Retrieval Augmented Generation for Dynamic Graph Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This limitation restricts the ability to adapt to new and emerging patterns in dynamic graphs. To address this challenge, we propose a novel framework, Retrieval-Augmented Generation for Dy namic Graph modeling (RAG4DyG ), which enhances dynamic graph predictions by incorporating contextually and temporally relevant examples from broader graph structures. |
Yuxia Wu; Lizi Liao; Yuan Fang; |
| 135 | Empowering Large Language Model Agent Through Step-Level Self-Critique and Self-Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce SLSC-MCTS, a method that integrates Monte Carlo Tree Search with Step-Level Self-Critique to enhance LLM agents during both testing and self-training phases. |
Yuanzhao Zhai; Huanxi Liu; Zhuo Zhang; Tong Lin; Kele Xu; Cheng Yang; Dawei Feng; Bo Ding; Huaimin Wang; |
| 136 | Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Concurrently, despite the proven effectiveness of large language models (LLMs), their integration into commercial recommender systems is impeded by issues such as high inference latency, incomplete capture of all distribution statistics, and catastrophic forgetting. To address these issues, we introduce a novel Pre-train, Align, and Disentangle (PAD) framework to enhance SR models with LLMs. |
Yuhao Wang; Junwei Pan; Pengyue Jia; Wanyu Wang; Maolin Wang; Zhixiang Feng; Xiaotian Li; Jie Jiang; Xiangyu Zhao; |
| 137 | Data Augmentation As Free Lunch: Exploring The Test-Time Augmentation for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore the test-time augmentation (TTA) for sequential recommendation, which augments the inputs during the model inference and then aggregates the model’s predictions for augmented data to improve final accuracy. |
Yizhou Dang; Yuting Liu; Enneng Yang; Minhan Huang; Guibing Guo; Jianzhe Zhao; Xingwei Wang; |
| 138 | Unleashing The Potential of Diffusion Models Towards Diversified Sequential Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such a practice neglects users’ heterogeneous preferences towards various types of items, further limiting recommendation diversity. To bridge these two critical gaps and to further unleash the potential of DMs in enhancing the recommendation diversity of SRSs, we propose a novel diversity-guided diffusion model for sequential recommendations, called DiffDiv for short. |
Zhuo Cai; Shoujin Wang; Victor W. Chu; Usman Naseem; Yang Wang; Fang Chen; |
| 139 | Denoising Multi-Interest-Aware Logical Reasoning for Long-Sequence Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: 2) They are often dominated by the user’s single primary interest, which prevents simultaneous consideration of users’ multiple-aspect interests in long sequences. To address these issues, we propose a novel dEnoising Multi-Interest-aware Logical rEasoning (EMILE) method for long-sequence recommendation. |
Fei Li; Qingyun Gao; Yizhou Dang; Enneng Yang; Guibing Guo; Jianzhe Zhao; Xingwei Wang; |
| 140 | X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents ”X-Cross” — a novel cross-domain sequential-recommendation model that recommends products in new domains by integrating several domain-specific language models; each model is fine-tuned with low-rank adapters (LoRA). |
Guy Hadad; Haggai Roitman; Yotam Eshel; Bracha Shapira; Lior Rokach; |
| 141 | Improving Sequential Recommenders Through Counterfactual Augmentation of System Exposure Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Even methods that consider system exposure typically train the recommender using only the logged historical system exposure, without exploring unseen user interests.In this paper, we propose counterfactual augmentation over system exposure for sequential recommendation (CaseRec). |
Ziqi Zhao; Zhaochun Ren; Jiyuan Yang; Zuming Yan; Zihan Wang; Liu Yang; Pengjie Ren; Zhumin Chen; Maarten de Rijke; Xin Xin; |
| 142 | Triplet Contrastive Learning with Learnable Sequence Augmentation for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Low-quality augmentation offers limited benefits for model optimization. Existing contrastive learning-based sequential recommendation works primarily utilize heuristic data augmentation methods, which often exhibit excessive randomness and struggle to generate positive samples that align with users’ true intentions.To address this limitation, we propose Triplet Contrastive learning with Learnable sequence Augmentation for sequential Recommendation (TCLARec). |
Wei Wang; Yujie Lin; Moyan Zhang; Hongyu Lu; Jianli Zhao; Jie Sun; Xianye Ben; Pengjie Ren; Yujun Li; |
| 143 | STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose STAR-Rec, a novel architecture that synergistically combines preference-aware attention and state-space modeling through a sequence-level mixture-of-experts framework. |
Maolin Wang; Sheng Zhang; Ruocheng Guo; Wanyu Wang; Xuetao Wei; Zitao Liu; Hongzhi Yin; Yi Chang; Xiangyu Zhao; |
| 144 | Towards Interest Drift-driven User Representation Learning in Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, interest drift presents two critical challenges for these SR methods: (1) how to explore the potential distributions of the users’ varying interest drift levels; and (2) how to capture the interest drift-aware collaborative knowledge among the users. In this paper, we delve into the issue of interest drift in SR and propose a novel and generic framework, i.e., Interest Drift-driven User Representation Learning (IDURL), to enhance SR methods to tackle the above two challenges. |
Xiaolin Lin; Weike Pan; Zhong Ming; |
| 145 | Intent-aware Diffusion with Contrastive Learning for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, using noisy augmented sequences in contrastive learning may mislead the model to focus on irrelevant features, distorting the embedding space and failing to capture users’ true behavior patterns and intents. To address these issues, we propose Intent-aware Diffusion with contrastive learning for sequential Recommendation (InDiRec). |
Yuanpeng Qu; Hajime Nobuhara; |
| 146 | CSRec: Rethinking Sequential Recommendation from A Causal Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, the impact of the RecSys itself on users’ decisions has not been appropriately isolated and quantitatively analyzed. To address these challenges, we propose a novel formulation of sequential recommendation, called Causal Sequential Recommendation. |
Xiaoyu Liu; Jiaxin Yuan; Yuhang Zhou; Jingling Li; Furong Huang; Wei Ai; |
| 147 | Multi-Grained Patch Training for Efficient LLM-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To overcome these, we propose PatchRec, a multi-grained patch training method consisting of two stages: (1) Patch Pre-training, which familiarizes LLMs with aggregated embeddings — patches, and (2) Patch Fine-tuning, which enables LLMs to capture time-aware significance in interaction history. |
Jiayi Liao; Ruobing Xie; Sihang Li; Xiang Wang; Xingwu Sun; Zhanhui Kang; Xiangnan He; |
| 148 | Bridge The Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With powerful representation and reasoning abilities, Large Language Models (LLMs) are promising to address these two problems by bridging the items and capturing the user’s preferences from a semantic view. Therefore, we propose an LLMs Enhanced Cross-domain Sequential Recommendation model (LLM4CDSR). |
Qidong Liu; Xiangyu Zhao; Yejing Wang; Zijian Zhang; Howard Zhong; Chong Chen; Xiang Li; Wei Huang; Feng Tian; |
| 149 | Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper focuses on the problem of multi-modal multi-behavior sequential recommendation, aiming to address the following challenges: (1) the lack of effective characterization of modal preferences across different behaviors, as user attention to different item modalities varies depending on the behavior; (2) the difficulty of effectively mitigating implicit noise in user behavior, such as unintended actions like accidental clicks; (3) the inability to handle modality noise in multi-modal representations, which further impacts the accurate modeling of user preferences. To tackle these issues, we propose a novel Multi-Modal Multi-Behavior Sequential Recommendation model (M3BSR). |
Xiaoxi Cui; Weihai Lu; Yu Tong; Yiheng Li; Zhejun Zhao; |
| 150 | Mitigating Distribution Shifts in Sequential Recommendation: An Invariance Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel framework called Invariant Learning for Distribution Shifts in SEquential RecommendAtion (IDEA) to develop robust sequential recommendation. |
Yuxin Liao; Yonghui Yang; Min Hou; Le Wu; Hefei Xu; Hao Liu; |
| 151 | AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing approaches face three key limitations: 1) the degradation of the semantic space when high-dimensional language embeddings are mapped to lower-dimensional ID embeddings, 2) the underutilization of language embeddings, and 3) the reliance on additional trainable parameters, such as an adapter, to bridge the gap between the semantic and behavior spaces. In this paper, we introduce AlphaFuse, a simple but effective language-guided learning strategy that addresses these challenges by learning ID embeddings within the null space of language embeddings. |
Guoqing Hu; An Zhang; Shuo Liu; Zhibo Cai; Xun Yang; Xiang Wang; |
| 152 | DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: (i) They fail to remove noisy signals in item sequence and (ii) they underutilize the potential of side-information integration. To tackle these issues, we propose a novel SISR model, Dual Side-Information Filtering and Fusion (DIFF), which employs frequency-based noise filtering and dual multi-sequence fusion. |
Hye-young Kim; Minjin Choi; Sunkyung Lee; Ilwoong Baek; Jongwuk Lee; |
| 153 | Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issues outlined, we propose a Diversity-aware Dual-promotion Sequential Poisoning attack method named DDSP for SRSs. |
Yuchuan Zhao; Tong Chen; Junliang Yu; Kai Zheng; Lizhen Cui; Hongzhi Yin; |
| 154 | Adaptive User Dynamic Interest Guidance for Generative Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the fixed number of interests predefined by existing models cannot adapt to the diverse preferences of users, making it difficult to further improve recommendation performance. To address these issues, we propose a novel generative sequential recommendation framework named ADIGRec (Adaptive User Dynamic Interest Guidance for Generative Sequential Recommendation), which adaptively focuses on users’ dynamic interest features. |
Kai Zhu; Jing Li; Jia Wu; Yue He; Jun Chang; Guohao Li; Shuyi Zhang; |
| 155 | Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, most models rely on item ID co-occurrence and overlook rich semantic details, limiting their ability to capture fine-grained item features. To address these challenges, we propose a novel hierarchical intent-guided optimization approach with pluggable LLM-driven semantic learning for session-based recommendations, called HIPHOP. |
Jinpeng Chen; Jianxiang He; Huan Li; Senzhang Wang; Yuan Cao; Kaimin Wei; Zhenye Yang; Ye Ji; |
| 156 | Linear Item-Item Models with Neural Knowledge for Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel SBR model, namely Linear Item-Item model with Neural Knowledge (LINK), which integrates both types of knowledge into a unified linear framework. |
Minjin Choi; Sunkyung Lee; Seongmin Park; Jongwuk Lee; |
| 157 | Exploring The Escalation of Source Bias in User, Data, and Recommender System Feedback Loop Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: From a long-term perspective, our experiments also show that when AIGC dominates the content ecosystem after a feedback loop, it can lead to a decline in recommendation performance. To address these issues, we propose a debiasing method based on L1-loss optimization to maintain long-term content ecosystem balance. |
Yuqi Zhou; Sunhao Dai; Liang Pang; Gang Wang; Zhenhua Dong; Jun Xu; Ji-Rong Wen; |
| 158 | FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With this understanding, we propose a novel aggregation paradigm named collaborative information aggregation, which focuses on sharing collaborative information rather than item parameters. Based on this new paradigm, we introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation. |
Mingzhe Han; Dongsheng Li; Jiafeng Xia; Jiahao Liu; Hansu Gu; Peng Zhang; Ning Gu; Tun Lu; |
| 159 | Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing methods have not fully harnessed the potential of LLMs, often constrained by limited input information or failing to fully utilize their advanced reasoning capabilities. To address these limitations, we introduce EXP3RT, a novel LLM-based recommender designed to leverage rich preference information contained in user and item reviews. |
Jieyong Kim; Hyunseo Kim; Hyunjin Cho; SeongKu Kang; Buru Chang; Jinyoung Yeo; Dongha Lee; |
| 160 | Enhancing New-item Fairness in Dynamic Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This leads to significant unfairness towards new-items, which could accumulate over the successive model updates, ultimately compromising the stability of the entire system. Therefore, we propose FairAgent, a reinforcement learning (RL)-based new-item fairness enhancement framework specifically designed for DRSs. |
Huizhong Guo; Zhu Sun; Dongxia Wang; Tianjun Wei; Jinfeng Li; Jie Zhang; |
| 161 | Fair Recommendation with Biased-Limited Sensitive Attribute Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes a new method, Multiple Prior-Guided Robust Optimization (MPR), to achieve fair recommendations under biased observations of sensitive attributes, without requiring real sensitive attribute distribution. |
Jizhi Zhang; Haoyu Shen; Tianhao Shi; Keqin Bao; Xin Chen; Yang Zhang; Fuli Feng; |
| 162 | Social Relation-Level Privacy Risks and Preservation in Social Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While prior research has primarily focused on user-level and interaction-level privacy risks, the social relation-level privacy risks remain largely unexplored. To fill this gap, we investigate social privacy risks through membership inference attacks (MIA) and propose a Social relation-level MIA (SMIA) framework. |
Xuhao Zhao; Zhongrui Zhang; Yanmin Zhu; Zhaobo Wang; Wenze Ma; Jiadi Yu; Feilong Tang; |
| 163 | NR4DER: Neural Re-ranking for Diversified Exercise Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They frequently face difficulties in adjusting to inactive students’ learning patterns and in accommodating individualized learning paces, resulting in limited accuracy and diversity in recommendations. To tackle these challenges, we propose Neural Re-ranking for Diversified Exercise Recommendation (in short, NR4DER). |
Xinghe Cheng; Xufang Zhou; Liangda Fang; Chaobo He; Yuyu Zhou; Weiqi Luo; Zhiguo Gong; Quanlong Guan; |
| 164 | FIM: Frequency-Aware Multi-View Interest Modeling for Local-Life Service Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a multi-view search strategy that decomposes users’ demands from different perspectives to separate their various periodic intentions. |
Guoquan Wang; Qiang Luo; Weisong Hu; Pengfei Yao; Wencong Zeng; Guorui Zhou; Kun Gai; |
| 165 | Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current approaches that directly leverage LLMs for user interest learning face limitations in handling long sequential behaviors, effectively extracting interest, and applying interest in practical scenarios. To address these issues, we propose a Hierarchical Tree Search-based User Lifelong Behavior Modeling framework (HiT-LBM). |
Yu Xia; Rui Zhong; Hao Gu; Wei Yang; Chi Lu; Peng Jiang; Kun Gai; |
| 166 | CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging The Gap Between Trajectory and Language Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, LLMs encounter two critical challenges: (1) a lack of intrinsic understanding of numeric spatiotemporal data, which hinders accurate modeling of users’ spatiotemporal distributions and preferences; and (2) an excessively large and unconstrained candidate POI space, which often results in random or irrelevant predictions. To address these issues, we propose a Collaborative Multi-Agent Framework for Next POI Prediction, named CoMaPOI. |
Lin Zhong; Lingzhi Wang; Xu Yang; Qing Liao; |
| 167 | Disentangled Graph Debiasing for Next POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes a new graph debiasing paradigm for POI recommendation, which disentangles causal and bias knowledge within spatio-temporal graphs, allowing for not only the mitigation of bias issues, but also the utilization of causal information from spatial and temporal perspectives. |
Hailun Zhou; Jiajie Xu; Qiaoming Zhu; Chengfei Liu; |
| 168 | CD-CDR: Conditional Diffusion-based Item Generation for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While this well-designed paradigm shows promising performance, existing methods require extra supervision signals (e.g. contrastive learning on domain-masked embeddings) to maintain unified distributions across domains, leading to an inherent trade-off between unified objectives and domain-specific preference modeling. To address these limitations, we propose CD-CDR (Conditional Diffusion-CDR), a novel approach that leverages a shared conditional diffusion model to learn unified item distributions and facilitate knowledge transfer across domains. |
Hanyu Li; Jiayu Li; Weizhi Ma; Peijie Sun; Haiyang Wu; Jingwen Wang; Yuekui Yang; Min Zhang; Shaoping Ma; |
| 169 | Enhancing Cross-Domain Recommendation with Plug-In Contrastive Representations from Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods primarily rely on ID information of users and items, resulting in the entanglement of these two representations and hindering effective knowledge transfer. Therefore, we present a novel plug-in contrastive learning for CDR (PicCDR), which utilizes textual semantics to disentangle and enhance domain-invariant and domain-specific representations via LLMs. |
Ke Wang; Ji Zhang; Kuan Liu; |
| 170 | You Are What You Bought: Generating Customer Personas for E-commerce Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To reduce overhead, GPLR applies LLM-based labeling to only a fraction of users and utilizes a random walk technique to predict personas for the remaining customers. To further enhance efficiency, we propose an approximate solution called RevAff for this random walk-based computation. |
Yimin Shi; Yang Fei; Shiqi Zhang; Haixun Wang; Xiaokui Xiao; |
| 171 | Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: These challenges result in significant performance degradation in realistic situations where modalities are missing. To address these issues, we propose Disentangling and Generating Modality Recommender (DGMRec), a novel framework tailored for missing modality scenarios. |
Jiwan Kim; Hongseok Kang; Sein Kim; Kibum Kim; Chanyoung Park; |
| 172 | COHESION: Composite Graph Convolutional Network with Dual-Stage Fusion for Multimodal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, powerful representation learning enhances modality fusion, while effective fusion improves representation quality. Stemming from these two processes, we introduce a COmposite grapH convolutional nEtwork with dual-stage fuSION for the multimodal recommendation, named COHESION. |
Jinfeng Xu; Zheyu Chen; Wei Wang; Xiping Hu; Sang-Wook Kim; Edith C. H. Ngai; |
| 173 | CDC: Causal Domain Clustering for Multi-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To effectively cluster domains, we propose Causal Domain Clustering (CDC). |
Huishi Luo; Yiqing Wu; Yiwen Chen; Fuzhen Zhuang; Deqing Wang; |
| 174 | MELON: Learning Multi-Aspect Modality Preferences for Accurate Multimedia Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we identify two key limitations in existing methods regarding preferences for modality features: (L1) although preferences for modality features is an important aspect of users’ preferences, existing methods only leverage neighbors with similar interactions and do not consider the neighbors who may have similar preferences for modality features while having different interactions; (L2) although modality features of a user and an item may have a complex geometric relationship in the latent space, existing methods overlook and face challenges in precisely capturing this relationship. To address these two limitations, we propose a novel multimedia recommendation framework, named MELON, which is based on two core ideas: (Idea 1) Modality-cEntered embedding extraction; (Idea 2) reLatiOnship-ceNtered embedding extraction. |
Dongho Jeong; Taeri Kim; Donghyeon Cho; Sang-Wook Kim; |
| 175 | Adaptive Graph Integration for Cross-Domain Recommendation Via Heterogeneous Graph Coordinators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, integrating such multi-domain knowledge for cross-domain recommendation remains challenging due to inherent disparities in user behavior and item characteristics and the risk of negative transfer, where irrelevant or conflicting information from the source domains adversely impacts the target domain’s performance. To tackle these challenges, we propose HAGO, a novel framework with Heterogeneous Adaptive Graph coOrdinators, which dynamically integrates multi-domain graphs into a cohesive structure. |
Hengyu Zhang; Chunxu Shen; Xiangguo Sun; Jie Tan; Yu Rong; Chengzhi Piao; Hong Cheng; Lingling Yi; |
| 176 | Intent Representation Learning with Large Language Model for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Exploring these multimodal intents, especially the inherent differences in representation spaces, poses two key challenges: i) How to align multimodal intents and effectively mitigate noise issues; ii) How to extract and match latent key intents across modalities. To tackle these challenges, we propose a model-agnostic framework, Intent Representation Learning with Large Language Model (IRLLRec), which leverages large language models (LLMs) to construct multimodal intents and enhance recommendations. |
Yu Wang; Lei Sang; Yi Zhang; Yiwen Zhang; |
| 177 | Short Video Segment-level User Dynamic Interests Modeling in Personalized Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To capture users’ dynamic segment interests, we propose an innovative model that integrates a hybrid representation module, a multi-modal user-video encoder, and a segment interest decoder. |
Zhiyu He; Zhixin Ling; Jiayu Li; Zhiqiang Guo; Weizhi Ma; Xinchen Luo; Min Zhang; Guorui Zhou; |
| 178 | Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Then, we discern its two characteristics: (1) the vast number of items and users in RSs leads to retrieval inefficiency, and (2) RSs exhibit high diversity tolerance for LLM-generated text. Building on these insights, we introduce Lossless Acceleration via Speculative Decoding for LLM-based Recommender Systems (LASER), which features a Customized Retrieval Pool to enhance retrieval efficiency and Relaxed Verification to improve the acceptance rate of draft tokens. |
Yunjia Xi; Hangyu Wang; Bo Chen; Jianghao Lin; Menghui Zhu; Weiwen Liu; Ruiming Tang; Zhewei Wei; Weinan Zhang; Yong Yu; |
| 179 | ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we unveil a critical yet overlooked risk: LLM-powered agents can be strategically deployed to attack ID-free recommenders, stealthily promoting low-quality items in black-box settings. |
Zongwei Wang; Min Gao; Junliang Yu; Xinyi Gao; Quoc Viet Hung Nguyen; Shazia Sadiq; Hongzhi Yin; |
| 180 | MSL: Not All Tokens Are What You Need for Tuning LLM As A Recommender Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: During implementation, we identify a potential challenge related to gradient vanishing of MSL. |
Bohao Wang; Feng Liu; Jiawei Chen; Xingyu Lou; Changwang Zhang; Jun Wang; Yuegang Sun; Yan Feng; Chun Chen; Can Wang; |
| 181 | Order-agnostic Identifier for Large Language Model-based Generative Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, we introduce a novel set identifier paradigm for LLM-based generative recommendation, representing each item as a set of order-agnostic tokens. |
Xinyu Lin; Haihan Shi; Wenjie Wang; Fuli Feng; Qifan Wang; See-Kiong Ng; Tat-Seng Chua; |
| 182 | Process-Supervised LLM Recommenders Via Flow-guided Tuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While large language models (LLMs) are increasingly adapted for recommendation systems via supervised fine-tuning (SFT), this approach amplifies popularity bias due to its likelihood maximization objective, compromising recommendation diversity and fairness. To address this, we present Flow-guided fine-tuning recommender (Flower), which replaces SFT with a Generative Flow Network (GFlowNet) [6] framework that enacts process supervision through token-level reward propagation. |
Chongming Gao; Mengyao Gao; Chenxiao Fan; Shuai Yuan; Wentao Shi; Xiangnan He; |
| 183 | Large Language Models Enhanced Hyperbolic Space Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a model-agnostic framework, named HyperLLM, which extracts and integrates hierarchical information from both structural and semantic perspectives. |
Wentao Cheng; Zhida Qin; Zexue Wu; Pengzhan Zhou; Tianyu Huang; |
| 184 | Modeling Social Behavior in Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a more comprehensive social behavior model that describes fine-grained relationships between user interest and item popularity. |
Yihong Zhang; Takahiro Hara; |
| 185 | Graph Spectral Filtering with Chebyshev Interpolation for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Building on spectral graph theory, we reveal that these limitations stem from graph filtering with a cut-off in the frequency spectrum and a restricted linear form. To address these issues, we introduce ChebyCF, a CF framework based on graph spectral filtering. |
Chanwoo Kim; Jinkyu Sung; Yebonn Han; Joonseok Lee; |
| 186 | Collaborative Diffusion Models for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, since these features often originate from heterogeneous semantic or modal spaces, they may include redundant or task-irrelevant information that can hinder the learning process. To address these issues, we propose the Collaborative Diffusion Models for Recommendation (CoDMR). |
Mengru Chen; Lianghao Xia; Yong Xu; Ronghua Luo; |
| 187 | Unveiling Contrastive Learning’s Capability of Neighborhood Aggregation for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We further substantiate this capability through experimental validation and identify common misconceptions in the selection of positive samples in previous methods, which limit the potential of CL objective. Based on this discovery, we propose the Light Contrastive Collaborative Filtering (LightCCF) method, which introduces a novel neighborhood aggregation objective to bring users closer to all interacted items while pushing them away from other positive pairs, thus achieving high-quality neighborhood aggregation with very low time complexity. |
Yu Zhang; Yiwen Zhang; Yi Zhang; Lei Sang; Yun Yang; |
| 188 | Social Context-Aware Community-Level Propagation Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While progress has been made in understanding user-level propagation, there is a significant gap in addressing the CLIPP problem at the community level, particularly with regard to social context interpretation and the cold start problem in niche communities. To bridge this gap, we propose a novel model, named Community-Level Propagation Prediction with LLM enhanced Social Context Interpretation and Community Coldstart (ComPaSC3), which integrates three primary modules. |
Jinfei Gao; Xiao Wang; Tian Gan; Jianhua Yin; Chuanchen Luo; Liqiang Nie; |
| 189 | Towards Distribution Matching Between Collaborative and Language Spaces for Generative Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The core reason lies in the mismatch between the input-output formats and semantics of generative models and LMs, making it challenging to achieve optimal alignment in the feature space. This work addresses this issue by proposing a model-agnostic generative recommendation framework called DMRec, which introduces a probabilistic meta-network to bridge the outputs of LMs with user interactions, thereby enabling an equivalent probabilistic modeling process. |
Yi Zhang; Yiwen Zhang; Yu Wang; Tong Chen; Hongzhi Yin; |
| 190 | Hypercomplex Knowledge Graph-Aware Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore the integration of hypercomplex algebras in KG-aware recommendation and propose a Hypercomplex Knowledge Graph-aware Recommender (HKGR) method. |
Anchen Li; Bo Yang; Huan Huo; Farookh Hussain; Guandong Xu; |
| 191 | Balancing Self-Presentation and Self-Hiding for Exposure-Aware Recommendation Based on Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Drawing inspiration from sociological theories of human interaction patterns-specifically how individuals balance self-presentation and self-hiding behaviors in social contexts-this paper proposes BPH4Rec, a novel Balancing self-Presentation and self-Hiding approach for exposure-aware Recommendation based on GCL. |
Leqi Zheng; Chaokun Wang; Ziyang Liu; Canzhi Chen; Cheng Wu; Hongwei Li; |
| 192 | Invariance Matters: Empowering Social Recommendation Via Graph Invariant Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we approach the social denoising problem from the perspective of graph invariant learning and propose a novel approach, Social Graph Invariant Learning(SGIL). |
Yonghui Yang; Le Wu; Yuxin Liao; Zhuangzhuang He; Pengyang Shao; Richang Hong; Meng Wang; |
| 193 | CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead, we propose to leverage LLMs’ reasoning abilities during the candidate filtering process, and introduce Chain Of Retrieval ON grAphs (CORONA) to progressively narrow down the range of candidate items on interaction graphs with the help of LLMs: (1) First, LLM performs preference reasoning based on user profiles, with the response serving as a query to extract relevant users and items from the interaction graph as preference-assisted retrieval�; (2) Then, using the information retrieved in the previous step along with the purchase history of target user, LLM conducts intent reasoning to help refine an even smaller interaction subgraph as intent-assisted retrieval�; (3) Finally, we employ a GNN to capture high-order collaborative filtering information from the extracted subgraph, performing GNN-enhanced retrieval to generate the final recommendation results. |
Junze Chen; Xinjie Yang; Cheng Yang; Junfei Bao; Zeyuan Guo; Yawen Li; Chuan Shi; |
| 194 | Bridging Short Videos and Streamers with Multi-Graph Contrastive Learning for Live Streaming Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MGCCDR, a Multi-Graph Contrastive learning framework for Cross-Domain Recommendation, which leverages both overlapping users and non-overlapping items to enhance information transfer. |
Changle Qu; Liqin Zhao; Yanan Niu; Xiao Zhang; Jun Xu; |
| 195 | Rating-Aware Homogeneous Review Graphs and User Likes/Dislikes Differentiation for Effective Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these GNN-based RBRS methods present two main issues: (1) by con- verting each review text into the weight, i.e., single value, of a edge between a user node and an item node, they lose the rich informa- tion about users and items inherent in the review; and (2) by creating only a single general representation for each user, they cannot repre- sent the individual effects of users’ likes and dislikes on their ratings for items they have interacted with. To address these problems, we propose a novel GNN-based RBRS, named LETTER, utilizing homo- geneous graphs, i.e., user-user graphs and an item-item graph, to learn general representations of users and items along with users’ like and dislike representations. |
Jiwon Son; Hyunjoon Kim; Sang-Wook Kim; |
| 196 | VoRec: Enhancing Recommendation with Voronoi Diagram in Hyperbolic Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose VoRec, a novel framework that explores the spatial distribution of items and their associated tags to achieve accurate recommendations in hyperbolic space. |
Yong Chen; Li Li; Wei Peng; Songzhi Su; |
| 197 | Embracing Plasticity: Balancing Stability and Plasticity in Continual Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose PlastIcity and StAbility balancing continual recommender systems (PISA), a novel framework that adaptively balances stability and plasticity based on user preference shifts. |
Hyunsik Yoo; SeongKu Kang; Ruizhong Qiu; Charlie Xu; Fei Wang; Hanghang Tong; |
| 198 | Efficient Recommendation with Millions of Items By Dynamic Pruning of Sub-Item Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By adapting dynamic pruning concepts from document retrieval, we propose the RecJPQPrune dynamic pruning algorithm to efficiently find the top highest-scored items without computing the scores of all items in the catalogue. |
Aleksandr V. Petrov; Craig Macdonald; Nicola Tonellotto; |
| 199 | Pre-training for Recommendation Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional approaches suffer from significant drawbacks: fragmentation methods damage graph structure and diminish performance, while influence function techniques make assumptions that may not hold in complex GNNs, particularly with self-supervised or random architectures. To address these limitations, we propose a novel model-agnostic pre-training paradigm UnlearnRec that prepares systems for efficient unlearning operations. |
Guoxuan Chen; Lianghao Xia; Chao Huang; |
| 200 | CSMF: Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such methods significantly increase the model parameters, leading to an increased retrieval latency and a limited ability to model causal relationships between objectives. To address these challenges, we propose the Cascaded Selective Mask Fine-Tuning (CSMF), a novel method that enhances both retrieval efficiency and serving performance for multi-objective EBR. |
Hao Deng; Haibo Xing; Kanefumi Matsuyama; Moyu Zhang; Jinxin Hu; Hong Wen; Yu Zhang; Xiaoyi Zeng; Jing Zhang; |
| 201 | Multi-scenario Instance Embedding Learning for Deep Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this ignores the information redundancy of the dimension set and the individuality of different instances in the same scenario. To address these limitations, we propose a multi-scenario instance embedding learning (MultiEmb) framework that implements exclusive feature-dimension redundant information removal for different instances within a scenario to obtain the optimal individual embeddings. |
Chaohua Yang; Dugang Liu; Xing Tang; Yuwen Fu; Xiuqiang He; Xiangyu Zhao; Zhong Ming; |
| 202 | Why Is Normalization Necessary for Linear Recommenders? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by this analysis, we propose a versatile normalization solution, called Data-Adaptive Normalization (DAN), which flexibly controls the popularity and neighborhood biases by adjusting item- and user-side normalization to align with unique dataset characteristics. |
Seongmin Park; Mincheol Yoon; Hye-young Kim; Jongwuk Lee; |
| 203 | Addressing Missing Data Issue for Diffusion-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Since missing data is uncertain in both occurrence and content, recovering it is impractical and may introduce additional errors. To tackle this challenge, we propose a novel dual-side Thompson sampling-based Diffusion Model (TDM), which simulates extra missing data in the guidance signals and allows diffusion models to handle existing missing data through extrapolation. |
Wenyu Mao; Zhengyi Yang; Jiancan Wu; Haozhe Liu; Yancheng Yuan; Xiang Wang; Xiangnan He; |
| 204 | Hyperbolic Multi-Criteria Rating Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Secondly, the inevitable noise in MC ratings may hinder the recommendation quality of the model. To address the above issues, we propose a novel framework called Hyperbolic Multi-Criteria Recommendation (HMCR), which aims to mine users’ MC behavioral features on hyperbolic manifolds and mitigate the noise interference through knowledge transfer among the criteria. |
Zhihao Guo; Ting Han; Peng Song; Chenjiao Feng; Kaixuan Yao; Jiye Liang; |
| 205 | MGIPF: Multi-Granularity Interest Prediction Framework for Personalized Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, it is considerably demanding in terms of data for most existing approaches to effectively model users’ multi-granularity interests with limited or no supporting examples, resulting in subpar performance due to the significant long-tail phenomenon. To tackle these issues, we propose a novel learning framework named the Multi-Granularity Interest Prediction Framework (MGIPF), for better modeling users’ diverse interests. |
Ruoxuan Feng; Zhen Tian; Qiushi Peng; Jiaxin Mao; Wayne Xin Zhao; Di Hu; Changwang Zhang; |
| 206 | A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, a novel architecture named learnable Fully Interacted Two-tower Model (FIT) is proposed, which enables rich information interactions while ensuring inference efficiency. |
Chao Xiong; Xianwen Yu; Wei Xu; Lei Cheng; Chuan Yuan; Linjian Mo; |
| 207 | DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, a dual-agent framework, DARLR, is proposed to dynamically update world models to enhance recommendation policies. |
Yi Zhang; Ruihong Qiu; Xuwei Xu; Jiajun Liu; Sen Wang; |
| 208 | DAR: Dimension-Adaptive Recommendation with Multi-Granular Noise Control Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our key insight is that noisy interactions often result from simple, impulsive decisions triggered by surface-level aspects and thus require less information to represent, while genuine preferences involve more complex considerations of aspects that need richer representations. Based on this insight, we propose DAR, a dimension-adaptive recommendation framework that dynamically adjusts each interaction’s representation dimension to achieve fine-grained denoising control (re-scaling). |
Riwei Lai; Li Chen; Rui Chen; Chi Zhang; |
| 209 | DLF: Enhancing Explicit-Implicit Interaction Via Dynamic Low-Order-Aware Fusion for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing two-stream architectures integrate these paradigms but face challenges such as limited information sharing, gradient imbalance, and difficulty preserving low-order signals in sparse CTR data. We propose a novel framework, DynamicLow-Order-Aware Fusion (DLF), which addresses these limitations through two key components: a Residual-Aware Low-Order Interaction Network (RLI) and a Network-Aware Attention Fusion Module (NAF). |
Kefan Wang; Hao Wang; Wei Guo; Yong Liu; Jianghao Lin; Defu Lian; Enhong Chen; |
| 210 | Killing Two Birds with One Stone: Unifying Retrieval and Ranking with A Single Generative Recommendation Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents the Unified Generative Recommendation Framework (UniGRF), a novel approach that integrates retrieval and ranking into a single generative model. |
Luankang Zhang; Kenan Song; Yi Quan Lee; Wei Guo; Hao Wang; Yawen Li; Huifeng Guo; Yong Liu; Defu Lian; Enhong Chen; |
| 211 | Agentic Feedback Loop Modeling Improves Recommendation and User Simulation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Towards this research gap, we propose a novel framework that emphasizes the feedback loop process to facilitate the collaboration between the recommendation agent and the user agent. |
Shihao Cai; Jizhi Zhang; Keqin Bao; Chongming Gao; Qifan Wang; Fuli Feng; Xiangnan He; |
| 212 | Learning to Rank with Variable Result Presentation Lengths Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Deciding on the document presentation lengths in a fixed vertical space ranking is an important problem that has not been addressed by existing LTR methods.We address this gap by introducing the variable presentation length ranking task, where simultaneously the ordering of documents and their presentation length is decided. |
Norman Knyazev; Harrie Oosterhuis; |
| 213 | QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. |
Shubham Chatterjee; Jeff Dalton; |
| 214 | Distributionally Robust Optimization for Unbiased Learning to Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we first validate and analyze the distribution shift problem with a real-world ULTR dataset. |
Zechun Niu; Lang Mei; Chong Chen; Jiaxin Mao; |
| 215 | Zero-Shot Reranking with Large Language Models and Precomputed Ranking Features: Opportunities and Limitations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing research, though, has been confined to unstructured text corpora, leaving a gap in our understanding: to what extent do the findings of zero-shot LLM rerankers established on plain text corpora hold for datasets containing predominantly precomputed ranking features as is common in industrial settings? We explore this question via an empirical study on one public learning-to-rank dataset (MSLR-WEB10K) and two datasets collected from an audio streaming platform’s search logs. |
Maria Movin; Claudia Hauff; |
| 216 | Breaking The Lens of The Telescope: Online Relevance Estimation Over Large Retrieval Sets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel paradigm for re-ranking called online relevance estimation that continuously updates relevance estimates for a query throughout the ranking process. |
Mandeep Rathee; Venktesh V; Sean MacAvaney; Avishek Anand; |
| 217 | Comprehensive List Generation for Multi-Generator Reranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that we can achieve a more efficient and effective list proposal with a multi-generator framework and provide empirical evidence on two public datasets and online A/B tests. |
Hailan Yang; Zhenyu Qi; Shuchang Liu; Xiaoyu Yang; Xiaobei Wang; Xiang Li; Lantao Hu; Han Li; Kun Gai; |
| 218 | Bridging Personalization and Control in Scientific Personalized Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we introduce a model for personalized search that enables users to control personalized rankings proactively. |
Sheshera Mysore; Garima Dhanania; Kishor Patil; Surya Kallumadi; Andrew McCallum; Hamed Zamani; |
| 219 | Reason-to-Rank: Distilling Direct and Comparative Reasoning from Large Language Models for Document Reranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Reason-to-Rank (R2R), a novel framework that separates direct relevance reasoning from comparison reasoning to provide both direct and comparitive explanations. |
Yuelyu Ji; Zhuochun Li; Rui Meng; Daqing He; |
| 220 | CoDIME: A Counterfactual Approach for Dimension Importance Estimation Through Click Logs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by counterfactual modelling, we introduce Counterfactual DIMEs (CoDIMEs), designed to leverage noisy implicit feedback to assess the importance of each dimension. |
Guglielmo Faggioli; Nicola Ferro; Raffaele Perego; Nicola Tonellotto; |
| 221 | Stitching Inner Product and Euclidean Metrics for Topology-aware Maximum Inner Product Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our investigation, grounded in graph-based search, reveals that different indexing and search strategies offer distinct advantages for MIPS, depending on the underlying data topology. Building on these insights, we introduce a novel graph-based index called Metric-Amphibious Graph (MAG) and a corresponding search algorithm, Adaptive Navigation with Metric Switch (ANMS). |
Tingyang Chen; Cong Fu; Xiangyu Ke; Yunjun Gao; Yabo Ni; Anxiang Zeng; |
| 222 | On The Scaling of Robustness and Effectiveness in Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent work has addressed scaling laws of effectiveness in dense retrieval, revealing a power-law relationship between effectiveness and the size of models and data. Does robustness follow scaling laws too? If so, can scaling improve both robustness and effectiveness together, or do they remain locked in a trade-off?To answer these questions, we conduct a comprehensive experimental study. |
Yu-An Liu; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Yixing Fan; Xueqi Cheng; |
| 223 | Optimizing Compound Retrieval Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on the optimization of compound retrieval system design which uniquely involves learning where to apply the component models and how to aggregate their predictions into a final ranking. |
Harrie Oosterhuis; Rolf Jagerman; Zhen Qin; Xuanhui Wang; |
| 224 | Hypencoder: Hypernetworks for Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a new paradigm; instead of representing a query as a vector, we use a small neural network that acts as a learned query-specific relevance function. This small neural network takes a document representation as input (in this work we use a single vector) and produces a scalar relevance score. |
Julian Killingback; Hansi Zeng; Hamed Zamani; |
| 225 | Precise Zero-Shot Pointwise Ranking with LLMs Through Post-Aggregated Global Context Information Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to improve the effectiveness of pointwise methods while preserving their efficiency through two key innovations: (1) We propose a novel Global-Consistent Comparative Pointwise Ranking (GCCP) strategy that incorporates global reference comparisons between each candidate and an anchor document to generate contrastive relevance scores. |
Kehan Long; Shasha Li; Chen Xu; Jintao Tang; Ting Wang; |
| 226 | Classifying Term Variants in Query Formulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study investigates how crowd workers formulate an initial query for a common information need described in a backstory, resulting in diverse query variations. |
Nuha Abu Onq; Mark Sanderson; Falk Scholer; |
| 227 | Towards Lossless Token Pruning in Late-Interaction Retrieval Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce three regularization losses, that induce a solution with high pruning ratios, as well as two pruning strategies. |
Yuxuan Zong; Benjamin Piwowarski; |
| 228 | Locality-Sensitive Indexing for Graph-Based Approximate Nearest Neighbor Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Locality-Sensitive Indexing for Graph-Based Search (or LIGS), which utilizes independent locality-sensitive hashing algorithms to simulate a proximity graph, on which a standard graph search can be performed. |
Jun Woo Chung; Huawei Lin; Weijie Zhao; |
| 229 | Constrained Auto-Regressive Decoding Constrains Generative Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we examine the inherent limitations of constrained auto-regressive generation from two essential perspectives: constraints and beam search. |
Shiguang Wu; Zhaochun Ren; Xin Xin; Jiyuan Yang; Mengqi Zhang; Zhumin Chen; Maarten de Rijke; Pengjie Ren; |
| 230 | Boosting Retrieval-Augmented Generation with Generation-Augmented Retrieval: A Co-Training Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite its advantages, RAG faces challenges: (i) the structural gap between traditional dense retrievers and autoregressive generators, and (ii) limited generation performance due to insufficient contextual guidance returned by the retriever. To tackle these limitations, we propose MINT, a novel framework that enhances RAG by co-training Retrieval-augMented generatIon and geNeration-augmented reTrieval (GAR). |
Yubao Tang; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Yixing Fan; Xueqi Cheng; |
| 231 | Unsupervised Corpus Poisoning Attacks in Continuous Space for Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We thus propose an optimization method that operates in the embedding space directly. |
Yongkang Li; Panagiotis Eustratiadis; Simon Lupart; Evangelos Kanoulas; |
| 232 | Hybrid Advertising in The Sponsored Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, each of these two advertising models appeals to distinct user groups, leading to low click-through rates when users encounter an undesirable advertising model. To address this limitation and enhance generality, we propose a novel advertising model called ”Hybrid Advertising”. |
Zhen Zhang; Weian Li; Yuhan Wang; Qi Qi; Kun Huang; |
| 233 | UPPR+: Scaling Uncertain Personalised PageRank Computation on Billion-Sized Graphs with Mutually Exclusive Edges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, UPPR lacks error guarantees, and struggles to scale on large graphs due to the high cost to precompute block matrix inverses over the certain part of the graph. To address these problems, we propose UPPR+, an efficient scheme to retrieve PPR on billion-scale uncertain graphs. |
Min Zhang; Weiren Yu; |
| 234 | WebANNS: Fast and Efficient Approximate Nearest Neighbor Search in Web Browsers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: WebANNS leverages WebAssembly to overcome computational bottlenecks, designs a lazy loading strategy to optimize data retrieval from external storage, and applies a heuristic approach to reduce memory usage. |
Mugeng Liu; Siqi Zhong; Qi Yang; Yudong Han; Xuanzhe Liu; Yun Ma; |
| 235 | TITE: Token-Independent Text Encoder for Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Token-Independent Text Encoder (TITE) as a more efficient modification of the backbone encoder model. |
Ferdinand Schlatt; Tim Hagen; Martin Potthast; Matthias Hagen; |
| 236 | WARP: An Efficient Engine for Multi-Vector Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Multi-vector retrieval methods such as ColBERT and its recent variant, the ConteXtualized Token Retriever (XTR), offer high accuracy but face efficiency challenges at scale. To address this, we present WARP, a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective through three key innovations: (1) WARPSELECT for dynamic similarity imputation; (2) implicit decompression, avoiding costly vector reconstruction during retrieval; and (3) a two-stage reduction process for efficient score aggregation. |
Jan Luca Scheerer; Matei Zaharia; Christopher Potts; Gustavo Alonso; Omar Khattab; |
| 237 | Highly Efficient Disk-based Nearest Neighbor Search on Extended Neighborhood Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper optimizes the disk-based NN search from three perspectives. |
Cheng Zhang; Jianzhi Wang; Wan-Lei Zhao; Shihai Xiao; |
| 238 | IGP: Efficient Multi-Vector Retrieval Via Proximity Graph Index Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a high-quality candidate generation technique that produces only hundreds of candidates yet achieves high recall. |
Zheng Bian; Man Lung Yiu; Bo Tang; |
| 239 | Efficient Re-ranking with Cross-encoders Via Early Exit Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Diverging from previous approaches, we propose Similarity-based Early Exit (SEE), a novel-non-learned-strategy that exploits the similarities between query and document token embeddings to early-terminate the inference of documents that will most likely be non-relevant to the query. |
Francesco Busolin; Claudio Lucchese; Franco Maria Nardini; Salvatore Orlando; Raffaele Perego; Salvatore Trani; Alberto Veneri; |
| 240 | Limitations of Automatic Relevance Assessments with Large Language Models for Fair and Reliable Retrieval Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, these correlations ignore whether and how LLM-based judgements impact the statistically significant differences among systems with respect to human assessments. In this work, we look at how LLM-generated judgements preserve ranking differences among top-performing systems and also how they preserve pairwise significance evaluation as human judgements. |
David Otero; Javier Parapar; \'{A}lvaro Barreiro; |
| 241 | HTGformer: Heterogeneous Temporal Graph Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite these successes, existing methods adopt independent parameterization strategies to handle various data distributions in HTGs, leading to optimization challenges and speed bottlenecks. To bridge this gap, this paper proposes a novel transformer-based representation learning paradigm for HTGs called HTGformer. |
Yili Wang; |
| 242 | Unbiased Collaborative Filtering with Fair Sampling Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we demonstrate that popularity bias arises from the influence of propensity factors during training. |
Jiahao Liu; Dongsheng Li; Hansu Gu; Peng Zhang; Tun Lu; Li Shang; Ning Gu; |
| 243 | Improving LLM-powered Recommendations with Personalized Information Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a pipeline called CoT-Rec, which integrates two key Chain-of-Thought (CoT) processes-user preference analysis and item perception analysis-into LLM-powered recommendations, thereby enhancing the utilization of LLMs’ reasoning abilities. |
Jiahao Liu; Xueshuo Yan; Dongsheng Li; Guangping Zhang; Hansu Gu; Peng Zhang; Tun Lu; Li Shang; Ning Gu; |
| 244 | AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the memory design in current methods causes user agents to introduce significant irrelevant information during decision-making in cross-domain scenarios and makes them unable to recognize the influence of other users’ interactions, such as popularity factors. To tackle this issue, we propose a dual-layer memory architecture combined with a two-step fusion mechanism. |
Jiahao Liu; Shengkang Gu; Dongsheng Li; Guangping Zhang; Mingzhe Han; Hansu Gu; Peng Zhang; Tun Lu; Li Shang; Ning Gu; |
| 245 | Exploring ℓ0 Sparsification for Inference-free Sparse Retrievers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore ℓ0 inspired sparsification manner for inference-free retrievers. |
Xinjie Shen; Zhichao Geng; Yang Yang; |
| 246 | Private Preferences, Public Rankings: A Privacy-Preserving Framework for Marketplace Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a privacy-preserving framework that integrates public seller rankings into personalized recommendations without exposing sensitive user preferences. |
Guilherme Ramos; Ludovico Boratto; Mirko Marras; |
| 247 | Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, we examine two model families (GPT and Llama) across multiple sizes, focusing on one of the most widely used dataset in recommender systems: MovieLens-1M. |
Dario Di Palma; Felice Antonio Merra; Maurizio Sfilio; Vito Walter Anelli; Fedelucio Narducci; Tommaso Di Noia; |
| 248 | NAM: A Normalization Attention Model for Personalized Product Search In Fliggy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Previous studies largely focused on the user factors when personalizing the search query, while ignoring the item perspective, which leads to the following two challenges that we summarize in this paper: First, previous approaches relying only on co-occurrence frequency tend to overestimate the conversion rates for popular items and underestimate those for long-tail items, resulting in inaccurate item similarities; Second, user purchasing propensity is highly heterogeneous according to the popularity of the target item: it is less correlated with the user’s historical behavior for a popular item and more correlated for a long-tail item. To address these challenges, in this paper we propose NAM, a Normalization Attention Model, which optimizes ”when to personalize” by utilizing Inverse Item Frequency (IIF) and employing a gating mechanism, as well as optimizes ”how to personalize” by normalizing the attention mechanism from a global perspective. |
Shui Liu; Mingyuan Tao; Maofei Que; Pan Li; Dong Li; Shenghua Ni; Zhuoran Zhuang; |
| 249 | Interest Changes: Considering User Interest Life Cycle in Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an effective method called Deep Interest Life-cycle Network (DILN), which not only captures the interest life-cycle features efficiently, but can also be easily integrated to existing ranking models. |
Yinjiang Cai; Jiangpan Hou; Yangping Zhu; Yuan Nie; |
| 250 | Counterfactual Model Selection in Contextual Bandits Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel counterfactual approach to address the model selection problem in contextual bandits. |
Shion Ishikawa; Young-joo Chung; Yun-Ching Liu; Yu Hirate; |
| 251 | Characterising Topic Familiarity and Query Specificity Using Eye-Tracking Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study demonstrates the feasibility of eye-tracking to better understand topic familiarity and query specificity in search. |
Jiaman He; Zikang Leng; Dana McKay; Johanne R. Trippas; Damiano Spina; |
| 252 | Improving Link Sign Prediction in Signed Bipartite Graphs Via Balanced Line Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Link sign prediction is a crucial task, but the link class imbalance when one type of link (e.g., the head class) is significantly more numerous than another (e.g., the tail class) makes this task extremely challenging. To address this challenge, we propose a Line-Graph-Based Dynamic Balancing Prediction (LDBP) method. |
Hongxiang Lin; Yixiao Zhou; Huiying Hu; Xiaoqing Lyu; |
| 253 | Multi-Interest Matching for Personalized News Recommendation with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Personalized news recommendation plays a vital role in mitigating information overload, yet challenges persist in accurately capturing user preferences and fine-grained interests. Leveraging the semantic understanding and extraction capabilities of large language models (LLMs), we propose a Multi-Interest Personalized News Recommendation (MIPNR) model to address these issues. |
Hongxiang Lin; Yixiao Zhou; Huiying Hu; Xiaoqing Lyu; |
| 254 | Score-Fitted Indexes and Constant Length Indexes for Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present two novel inverted index approaches and corresponding query processing strategies for information retrieval: 1) score-fitted indexes and 2) constant length indexes. |
Djoerd Hiemstra; |
| 255 | Evaluating Multi-Dimensional Cumulated Utility in Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show the practical feasibility and validity of the MDCU by applying it to publicly available TREC test collections. |
Francesco Luigi De Faveri; Guglielmo Faggioli; Nicola Ferro; Kalervo J\{a}rvelin; |
| 256 | Generating Effective Health-Related Queries for Promoting Reliable Search Results Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a method leveraging Large Language Models to generate synthetic narratives that guide the creation of alternative queries. |
Xiana Carrera; Marcos Fern\'{a}ndez-Pichel; David E. Losada; |
| 257 | RE-AdaptIR: Improving Information Retrieval Through Reverse Engineered Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore the effectiveness of extending reverse engineered adaptation to the context of information retrieval (RE-AdaptIR). |
William Fleshman; Benjamin Van Durme; |
| 258 | Bias in Language Models: Interplay of Architecture and Data? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through a novel attention weight analysis, we reveal distinct attention patterns for biased versus neutral content, offering insights into the internal representations learned by PLMs. |
Mozhgan Talebpour; Yunfei Long; Alba G. Seco De Herrera; Shoaib Jameel; |
| 259 | Dynamic Margin-based Contrastive Learning for Robust Negative Sampling in Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes dynamic margin-based contrastive learning (DMCL), which adaptively adjusts the decision boundary based on query-negative similarity, ensuring consistent exposure to moderately hard negatives. |
Tsai-Tsung Chen; Chuan-Ju Wang; Ming-Feng Tsai; |
| 260 | UTCS: Effective Unsupervised Temporal Community Search with Pre-training of Temporal Dynamics and Subgraph Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the first stage, we introduce multiple learning objectives to facilitate the pre-training process in the unsupervised learning setting. |
Yue Zhang; Yankai Chen; Yingli Zhou; Yucan Guo; Xiaolin Han; Chenhao Ma; |
| 261 | EIoU-EMC: A Novel Loss for Domain-specific Nested Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we design a novel loss EIoU-EMC, by enhancing the implement of Intersection over Union loss and Multi-class loss. |
Jian Zhang; Tianqing Zhang; Qi Li; Hongwei Wang; |
| 262 | Graph-Based Multimodal Contrastive Learning for Chart Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces a novel joint multimodal scene graph framework that explicitly models the relationships among chart components and their underlying structures. |
Yue Dai; Soyeon Caren Han; Wei Liu; |
| 263 | ELEC: Efficient Large Language Model-Empowered Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to leverage the benefits of both types of models and pursue collaboration, semantics and efficiency. |
Rui Dong; Wentao Ouyang; Xiangzheng Liu; |
| 264 | Exploring Human-Like Thinking in Search Simulations with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we explore the integration of human-like thinking into search simulations by leveraging LLMs to simulate users’ hidden cognitive processes. |
Erhan Zhang; Xingzhu Wang; Peiyuan Gong; Zixuan Yang; Jiaxin Mao; |
| 265 | Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we show how the Rational Speech Acts (RSA), a linguistics framework used to minimize the number of features to be communicated when identifying an object in a set, can be adapted to the IR case – and in particular to the high number of potential features (here, tokens). |
Arthur Satouf; Gabriel Ben-Zenou; Benjamin Piwowarski; Habiboulaye Amadou-Boubacar; Pablo Piantanida; |
| 266 | Scaling Sparse and Dense Retrieval in Decoder-Only LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we conduct a systematic comparative study on how different retrieval paradigms (sparse vs. dense) and fine-tuning objectives (CL vs. KD vs. their combination) affect retrieval performance across different model scales. |
Hansi Zeng; Julian Killingback; Hamed Zamani; |
| 267 | Dual Debiasing in LLM-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, LLM-based recommendation (LR) suffers from more severe popularity bias than conventional recommendation (CR), stemming from both training and inference stages. In this paper, we propose a novel debiasing method for LR, which performs debiasing in such two stages, so termed as Dual Debiasing in LR (D²LR). |
Sijin Lu; Zhibo Man; Fangyuan Luo; Jun Wu; |
| 268 | PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While simulation frameworks can generate synthetic data to address these gaps, existing methods fail to replicate behavioural diversity, limiting their effectiveness. To overcome these challenges, we propose the Personality-driven User Behaviour Simulator (PUB), an LLM-based simulation framework that integrates the Big Five personality traits to model personalised user behaviour. |
Chenglong Ma; Ziqi Xu; Yongli Ren; Danula Hettiachchi; Jeffrey Chan; |
| 269 | Learning Resistant Binary Descriptors Against Noise for Efficient Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose a noise-resistant Hashing Contrastive learning with hybrid selection (STAR). |
Qingqing Long; Haixin Wang; Jinan Sun; Wei Xiang; Yijia Xiao; Yusheng Zhao; Xiao Luo; |
| 270 | Graph-Augmented Retrieval with Memory-Driven Reasoning and Constraint-Aware Filtering for MultiHop QA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It demands the ability to retrieve and integrate dispersed knowledge across multiple documents dynamically while maintaining coherence in multi-step reasoning process. This study addresses these challenges with three primary contributions. It explores the integrating of large language models with graph-augmented retrieval methods for complex multihop reasoning. |
Siyuan Li; Yongping Du; Mingyang Li; |
| 271 | Template-Based Financial Report Generation in Agentic and Decomposed Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper investigates two LLM-based approaches popular in industry for generating templated financial reports: an agentic information retrieval (IR) framework and a decomposed IR approach, namely AgenticIR and DecomposedIR. |
Yong-En Tian; Yu-Chien Tang; Kuang-Da Wang; An-Zi Yen; Wen-Chih Peng; |
| 272 | A Comparative Study of Large Language Models and Traditional Privacy Measures to Evaluate Query Obfuscation Approaches Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such privacy assessments employ lexical and semantic similarity measures between the original and obfuscated queries. In this study, we explore the role of Large Language Models (LLMs) in privacy evaluation, simulating a scenario where users employ such models to determine whether their input has been effectively privatized. |
Francesco Luigi De Faveri; Guglielmo Faggioli; Nicola Ferro; |
| 273 | From Monolith to Mosaic: Uncovering Behavioral Differences for Choice Models in Recommender Systems Simulations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This one-size-fits-all approach overlooks the diversity in user preferences and decision-making patterns. In this work, we scrutinize whether this universal view fails to account for unique user behavior, thus harming realism and reliability of simulation outcomes. |
Robin Ungruh; Alejandro Bellog\'{\i}n; Maria Soledad Pera; |
| 274 | GINGER: Grounded Information Nugget-Based Generation of Responses Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address them, we propose a modular pipeline for grounded response generation that operates on information nuggets – minimal, atomic units of relevant information extracted from retrieved documents. |
Weronika \L{}ajewska; Krisztian Balog; |
| 275 | Interactive Code Information Integrated Programming Knowledge Tracing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing PKT models only rely on pre-trained language models to capture static code information, overlooking feedback scores and problem-specific context. To address this issue, we propose an Interactive Information integrated Code Embedding for Programming Knowledge Tracing (IICE-PKT). |
Xueqiang Zeng; Guangcheng Fu; Haofei Chen; Qiyun Peng; Mingwen Wang; |
| 276 | Retrieving Tables Via Inter- and Intra-Content Contrastive Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, many current table retrieval methods still operate on query-table joint encoding, which introduces notable inefficiencies during both the training and retrieval processes. For this issue, this paper proposes ConTR, a tabular semantic contrastive learning method that simultaneously considers both inter-table and intra-table differences. |
Jie Wu; Mengshu Hou; |
| 277 | Investigating Task Arithmetic for Zero-Shot Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we investigate Task Arithmetic, a technique that combines the weights of LLMs pre-trained on different tasks or domains via simple mathematical operations, such as addition or subtraction, to adapt retrieval models without requiring additional fine-tuning. |
Marco Braga; Pranav Kasela; Alessandro Raganato; Gabriella Pasi; |
| 278 | Echoes in The Feed: Evolution-aware Prompt-augmented Micro-video Popularity Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Current works in MVPP utilized the pre-trained vision-language models (PVLs) to model the multimodal features for prediction, failing to capture the evolving popularity trend in micro-videos and leading to suboptimal results. To tackle this limitation, we propose EvoPro, an Evolution-aware Prompt-augmented framework that enhances MVPP. |
Wei Chen; Jiao Li; Jian Lang; Zhangtao Cheng; Yong Wang; Fan Zhou; |
| 279 | Efficient Conversational Search Via Topical Locality in Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While previous efforts have primarily focused on improving effectiveness and user satisfaction, response time remains a critical bottleneck of conversational search systems. To address this, we exploit the topical locality inherent in conversational queries, i.e., the tendency of queries within a conversation to focus on related topics. |
Cristina Ioana Muntean; Franco Maria Nardini; Raffaele Perego; Guido Rocchietti; Cosimo Rulli; |
| 280 | SMMR: Sampling-Based MMR Reranking for Faster, More Diverse, and Balanced Recommendations and Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose Sampled Maximal Marginal Relevance (SMMR), a novel sampling-based extension of MMR that introduces randomness into item selection to improve relevance-diversity trade-offs. |
Kiryl Liakhnovich; Oleg Lashinin; Andrei Babkin; Michael Pechatov; Marina Ananyeva; |
| 281 | Assessing Support for The TREC 2024 RAG Track: A Large-Scale Comparative Study of LLM and Human Evaluations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conducted a comparative study of submissions to the TREC 2024 RAG Track, evaluating an automatic LLM judge (GPT-4o) against human judges for support assessment. |
Nandan Thakur; Ronak Pradeep; Shivani Upadhyay; Daniel Campos; Nick Craswell; Ian Soboroff; Hoa Trang Dang; Jimmy Lin; |
| 282 | Large Models Are Good Annotators for Zero-Shot Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a minimally supervised yet effective approach: GPT- and CLIP-powered attributes (GCAtt). |
Qingzhi He; Yizhen Jia; Wentong Li; Shengcai Liao; Rong Quan; Tong Cui; Jie Qin; |
| 283 | Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This problem limits the model’s ability to effectively leverage crucial features, resulting in suboptimal performance. To address this, we propose a Weighted Graph Contrastive Learning framework (WeightedGCL). |
Zheyu Chen; Jinfeng Xu; Yutong Wei; Ziyue Peng; |
| 284 | Bias-Aware Curriculum Sampling For Fair Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel curriculum-based training approach that manages bias exposure throughout the training process. |
Shirin Seyedsalehi; Hai Son Le; Morteza Zihayat; Ebrahim Bagheri; |
| 285 | Automatic Document Editing for Improved Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a study of using large language models (LLMs) to modify a document so as to have it highly ranked for a query by an undisclosed ranking function. |
Niv Bardas; Tommy Mordo; Oren Kurland; Moshe Tennenholtz; |
| 286 | A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To assess the robustness and reliability of LLM-based relevance judgments, we systematically investigate impact of prompt sensitivity on the task. |
Negar Arabzadeh; Charles L.A. Clarke; |
| 287 | An Alternative to FLOPS Regularization to Effectively Productionize SPLADE-Doc Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the issue of high DFs, we present a new variant of FLOPS regularization: DF-FLOPS. |
Aldo Porco; Dhruv Mehra; Igor Malioutov; Karthik Radhakrishnan; Moniba Keymanesh; Daniel Preo\c{t}iuc-Pietro; Sean MacAvaney; Pengxiang Cheng; |
| 288 | VAP3: Variation-Aware Prompt Performance Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose VAP3 (Variation-Aware Prompt Performance Prediction), a novel pre-generation PPP approach that integrates prompt variations with adversarial training to enhance robustness against trivial modifications and better capture prompt sensitivity. |
Negar Arabzadeh; Ebrahim Bagheri; |
| 289 | Translative Neural Team Recommendation: From Multilabel Classification to Sequence Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to reformulate the team recommendation problem into a sequence prediction task and leverage seq-to-seq models, including transformers, to map an input sequence of the required subset of skills onto an output sequence of the optimum subset of experts. |
Kap Thang; Hawre Hosseini; Hossein Fani; |
| 290 | Fact-Level Calibration and Correction for Long-Form Generations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on this framework, we introduce CARE (Confidence-Aware Fact Correction), a method that leverages high-confidence facts to iteratively refine and correct low-confidence ones. |
Yige Yuan; Bingbing Xu; Hexiang Tan; Fei Sun; Teng Xiao; Wei Li; Huawei Shen; Xueqi Cheng; |
| 291 | System Comparison Using Automated Generation of Relevance Judgements in Multiple Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Using relevance judgements and ranked retrieval runs from the TREC NeuCLIR track, this paper shows that LLMs can also produce reliable assessments in other languages, even when the topic description or the prompt are in a language different from the documents. |
Paul Thomas; Douglas W. Oard; Eugene Yang; Dawn Lawrie; James Mayfield; |
| 292 | Dual-perspective Data Augmentation and Curriculum Learning Framework for Low-resource Complex Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address it, we propose a dual-perspective data augmentation and curriculum learning framework. |
Mengxiao Song; Tianyun Liu; Wenyuan Zhang; Quangang Li; Tingwen Liu; |
| 293 | FROG: Effective Friend Recommendation in Online Games Via Modality-aware User Preferences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, none of existing approaches can effectively incorporate the multi-modal user features (e.g., images and texts) with the structural information in the friendship graph, due to the following limitations: (1) some of them ignore the high-order structural proximity between users (2) some fail to learn the pairwise relevance between users at modality-specific level, and (3) some cannot capture both the local and global user preferences on different modalities. By addressing these issues, in this paper, we propose an end-to-end model FROG that better models the user preferences on potential friends. |
Qiwei Wang; Dandan Lin; Wenqing Lin; Ziming Wu; |
| 294 | Text Obsoleteness Detection Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a Multitask learning framework that uses Large Language Models (LLMs) for two tasks: semantic update detection and semantic update necessity prediction. |
Rishav Ranaut; Sriparna Saha; Adam Jatowt; Manish Gupta; |
| 295 | Response Quality Assessment for Retrieval-Augmented Generation Via Conditional Conformal Factuality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent methods that attempt to improve RAG trustworthiness, such as through auto-evaluation metrics, lack probabilistic guarantees or require ground truth answers. To address these limitations, we propose Conformal-RAG, a novel framework inspired by recent applications of conformal prediction (CP) on large language models (LLMs). |
Naihe Feng; Yi Sui; Shiyi Hou; Jesse C. Cresswell; Ga Wu; |
| 296 | Conversational Argument Search Under Selective Exposure: Strategies for Balanced Perspective Access Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conversational argument search systems influence how users access diverse perspectives but are prone to selective exposure. To address this, we propose two strategies: an interface-level multi-agent framework that structures perspective presentation and an interaction-level questioning strategy that encourages deeper engagement. |
Kyusik Kim; Jeongwoo Ryu; Dongseok Heo; Hyungwoo Song; Changhoon Oh; Bongwon Suh; |
| 297 | LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce LREA, a novel attention mechanism that overcomes the limitations of existing approaches while ensuring computational efficiency. |
Xin Song; Xiaochen Li; Jinxin Hu; Hong Wen; Zulong Chen; Yu Zhang; Xiaoyi Zeng; Jing Zhang; |
| 298 | ReCDAP: Relation-based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous few-shot knowledge graph completion studies focused exclusively on the positive triple information that exists in the graph or, when negative triples were incorporated, used them merely as a signal to indicate incorrect triples. To overcome this limitation, we propose Relation-Based Conditional Diffusion with Attention Pooling (ReCDAP). |
Jeongho Kim; Chanyeong Heo; Jaehee Jung; |
| 299 | Multilingual Evaluation of Main Content Extractors for Web Pages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We analyze extractor performance across five languages-Greek, English, Polish, Russian, and Chinese-highlighting the need to adapt extraction models to linguistic variations. |
Aur\'{e}lien Bournonville; Ga\{e}l Dias; Thomas Largillier; Emmanuel Marchand; Fabrice Maurel; Guillaume Pitel; Fran\c{c}ois Rioult; |
| 300 | Large Language Model Relevance Assessors Agree With One Another More Than With Human Assessors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the agreement between different LLM~assessors has not yet been systematically investigated. To close this gap, we compare eight LLM~assessors on the TREC DL tracks and the retrieval task of the RAG track with each other and with human assessors. |
Maik Fr\{o}be; Andrew Parry; Ferdinand Schlatt; Sean MacAvaney; Benno Stein; Martin Potthast; Matthias Hagen; |
| 301 | Axiomatic Re-Ranking for Argument Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose new axioms that focus on the scenario of argument retrieval: retrieval for queries that need arguments in the results. |
Maximilian Heinrich; Marvin Vogel; Alexander Bondarenko; Matthias Hagen; Benno Stein; |
| 302 | GEAR: Generalized Alternating Regressor for Multi-Behavior Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While existing multi-behavior sequential recommendation methods attempt to capture these signals, they often suffer from fragmented modeling, such as decoupling behaviors and items into separate sequences, neglecting time-aware transitions, or relying on computationally intensive architectures that hinder real-world scalability. To address these limitations, we propose GEneralized Alternating Regressor (GEAR), a novel framework that unifies behaviors, items, and temporal contexts into a single autoregressive sequence through an alternating architecture. |
Junzhe Jiang; Kai Zhang; Junfeng Kang; Yucong Luo; Min Gao; |
| 303 | Measuring Hypothesis Testing Errors in The Evaluation of Retrieval Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We quantify Type II errors and propose that balanced classification metrics, such as balanced accuracy, can be used to portray the discriminative power of qrels. |
Jack McKechnie; Graham McDonald; Craig Macdonald; |
| 304 | Hierarchical User Long-term Behavior Modeling for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Secondly, the GSU retrieves only a subset of items from the user’s behavior sequence, ignoring the evolution of user interests and the interrelationships between different points of interest. To overcome these challenges, we propose a novel end-to-end hierarchical user long-term behavior modeling network for CTR prediction (HBM). |
Mao Pan; Xuanhua Yang; Nan Qiao; Dongyue Wang; Feng Mei; Xiwei Zhao; Sulong Xu; |
| 305 | Training-free Periodic Interest Augmentation in Incremental Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we have observed that the data distribution of real systems exhibits periodic drifts, leading to periodic fluctuations of prediction bias. To alleviate the above bias fluctuations while minimizing the loss of recent interests, we propose TPIA, a Training-free approach for Periodic Interest Augmentation in incremental recommendation. |
Heyuan Huang; Xingyu Lou; Changwang Zhang; Chaochao Chen; Kuiyao Dong; Feng Lu; Han Lei; Yihao Wang; Wangchunshu Zhou; Jun Wang; |
| 306 | In-Context Learning As An Effective Estimator of Functional Correctness of LLM-Generated Code Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an in-context learning (ICL) based approach for code quality estimation. |
Susmita Das; Madhusudan Ghosh; Priyanka Swami; Debasis Ganguly; Gul Calikli; |
| 307 | More Than Just A Conversation: A Multi-agent Reasoning Graph Knowledge Distillation for Conversational Stance Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As the number of dialogue turns increases and the conversation content becomes more complex, existing methods that simply incorporate conversational context are insufficient to effectively capture the nuanced information necessary for accurate stance detection. To address this issue, we introduce a Multi-agent Reasoning Graph Knowledge Distillation (MRGKD) framework, leveraging conversational reasoning among multiple Large Language Models (LLMs) into smaller language models. |
Bingbing Wang; Zhixin Bai; Qianlong Wang; Jingjie Lin; Min Yang; Xi Zeng; Ruifeng Xu; |
| 308 | Lost in Transliteration: Bridging The Script Gap in Neural IR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explore whether adapting the popular ”translate-train” paradigm to transliterations can enhance the robustness of multilingual Information Retrieval (IR) methods and bridge the gap between native and transliterated scripts. |
Andreas Chari; Iadh Ounis; Sean MacAvaney; |
| 309 | SEALR: Sequential Emotion-Aware LLM-Based Personalized Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study proposes the Sequential Emotion-Aware LLM-Based Personalized Recommendation System (SEALR ) to leverage sentiment analysis in user-generated reviews, tracking emotional changes and extracting sentiment labels. |
Namjun Lee; Jaekwang Kim; |
| 310 | Meta-Learning for Incomplete Multimodal Sentiment Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing efforts, trained and evaluated under fixed missing rates, struggle to adapt to real-world scenarios with varying missing rates. To address this, we propose the Missing Modality Adaptation Framework (M2AF), leveraging model-agnostic meta-learning to enhance robustness against different levels of modality incompleteness. |
Geng Tu; Tianhao Wu; Xuan Luo; Xi Zeng; Wenjie Li; Ruifeng Xu; |
| 311 | Document Similarity Enhanced IPS Estimation for Unbiased Learning to Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we argue that low-ranked documents that are similar to highly-ranked relevant documents are also likely to be relevant. |
Zeyan Liang; Graham McDonald; Iadh Ounis; |
| 312 | Balancing Precision and Generalization: Dynamic Instruction Generation for Model Adaptive Zero-Shot Reasoning in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Manually crafted instructions tailored to specific LLMs and tasks improve performance but reduce generalizability, while more general instructions lack detail and lower performance. To address this, we propose a dynamic instruction-generation method using an Instruction-Generation Prompt (IGP). |
Ruihan Zhu; Bo Wang; Dongming Zhao; Jing Liu; Ruifang He; Yuexian Hou; |
| 313 | Generate-Distill: Training Cross-Language IR Models with Synthetically-Generated Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: An alternative is to train on naturally-occurring documents and synthetically-generated queries. Generate-Distill uses this approach with state-of-the-art distillation methods to match the effectiveness of training with translated MS~MARCO across different domains. |
Dawn Lawrie; Efsun Kayi; Eugene Yang; James Mayfield; Douglas W. Oard; Scott Miller; |
| 314 | Evaluating Contrastive Feedback for Effective User Simulations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This study attempts to simulate a knowledge state by enhancing the model with additional implicit contextual information gained during the simulation. |
Andreas Konstantin Kruff; Timo Breuer; Philipp Schaer; |
| 315 | Effective Inference-Free Retrieval for Learned Sparse Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct an extended evaluation of regularization approaches for LSR where we discuss their effectiveness, efficiency, and out-of-domain generalization capabilities. |
Franco Maria Nardini; Thong Nguyen; Cosimo Rulli; Rossano Venturini; Andrew Yates; |
| 316 | Evaluating LLMs’ (In)ability to Follow Prompts in QA Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address our research question, we propose Oedipus, an evaluation framework to evaluate LLMs’ ability to follow prompts. |
Aparup Khatua; Tobias Kalmbach; Prasenjit Mitra; Sandipan Sikdar; |
| 317 | Are Information Retrieval Approaches Good at Harmonising Longitudinal Surveys in Social Science? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper investigates multiple unsupervised approaches on a survey dataset spanning 1946-2020, including probabilistic models, linear probing of language models, and pre-trained neural networks specialised for IR. |
Wing Yan Li; Zeqiang Wang; Jon Johnson; Suparna De; |
| 318 | Retrieving The Right Law: Enhancing Legal Search with Style Translation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce the Legal Query-to-Provision Retrieval (LQPR) task and construct Query2Provision (Q2P), a dataset designed to enhance law retrieval by incorporating diverse case scenarios and linguistic structures representative of real-world legal inquiries. |
Szu-Ju Chen; Jing Jin; Sheng-Lun Wei; Chien-Hung Chen; Hsin-Hsi Chen; |
| 319 | Reinforcement Learning for Effective Few-Shot Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the sample inefficiency of neural ranking methods by introducing a Reinforcement Learning (RL)-based re-ranking model that achieves high effectiveness with minimal training data. |
Shiva Soleimany; Sajad Ebrahimi; Shirin Seyedsalehi; Fattane Zarrinkalam; Ebrahim Bagheri; |
| 320 | Exploring The Role of Diversity in Example Selection for In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We posit that reranking the retrieved context to enhance topical diversity can improve downstream task performance. To achieve this, we leverage maximum marginal relevance (MMR) which balances topical similarity with inter-example diversity. |
Janak Kapuriya; Manit Kaushik; Debasis Ganguly; Sumit Bhatia; |
| 321 | Refining Fidelity Metrics for Explainable Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Counterfactual evaluation provides a promising framework for assessing explanation fidelity in recommender systems, but per- turbation metrics adapted from computer vision suffer three key limitations: (1) they conflate explaining and contradictory features (2) they average over entire user histories instead of prioritizing concise, high-impact explanations, and (3) they use fixed-percentage perturbations, leading to inconsistencies across users.We introduce refined counterfactual metrics that focus on the most relevant explaining features, exclude contradictory elements, and assess fidelity at a fixed explanation length, ensuring a more consistent and interpretable evaluation. |
Mikhail Baklanov; Veronika Bogina; Yehonatan Elisha; Yahlly Schein; Liron Allerhand; Oren Barkan; Noam Koenigstein; |
| 322 | Towards Best Practices of Axiomatic Activation Patching in Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, based on experimental results, we provide recommendations on measuring patching effects and designing diagnostic datasets for investigating term frequency. |
Gregory Polyakov; Catherine Chen; Carsten Eickhoff; |
| 323 | In A Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we empirically compare user intent classification into informational, navigational, and transactional categories, using weak supervision and LLMs. |
Daria Alexander; Arjen P. de Vries; |
| 324 | Aligning Web Query Generation with Ranking Objectives Via Direct Preference Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast, we propose a framework that leverages Direct Preference Optimization (DPO) to integrate ranking signals into the query generation process, aiming to directly optimize the model towards generating high-quality queries that maximize downstream retrieval effectiveness. |
Jo\~{a}o Coelho; Bruno Martins; Jo\~{a}o Magalh\~{a}es; Chenyan Xiong; |
| 325 | Low-Cost Document Retrieval with Dense Pseudo-Query Encoding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a staged sparse-to-dense retrieval framework that substitutes expensive dense query encoding with a dense pseudo-query (DPQ), an approximation derived solely from sparse retrieval results. |
Shanxiu He; Wentai Xie; Yifan Qiao; Parker Carlson; Tao Yang; |
| 326 | Measuring The Fairness Gap Between Retrieval and Generation in RAG Systems Using A Cognitive Complexity Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the problem of quantifying fairness in Retrieval-Augmented Generation (RAG) systems, particularly for complex cognitive tasks that go beyond factual question-answering. |
Sandeep Avula; Chia-Jung Lee; Rongting Zhang; Vanessa Murdock; |
| 327 | Fast and Effective Early Termination for Simple Ranking Functions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such simple ranking functions usually compute the score of a document as the sum of term-wise impact scores, and they include traditional baselines such as BM25 and Query Likelihood, as well as some recently proposed learned sparse models based on document expansion and learned impact scores. In this paper, we explore extremely fast and highly effective early termination techniques for such simple ranking functions. |
Jinrui Gou; Antonio Mallia; Yifan Liu; Minghao Shao; Torsten Suel; |
| 328 | Dynamic Superblock Pruning for Fast Learned Sparse Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes superblock pruning (SP) during top-k online document retrieval for learned sparse representations. |
Parker Carlson; Wentai Xie; Shanxiu He; Tao Yang; |
| 329 | A Large-Scale Study of Reranker Relevance Feedback at Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study inference-time reranker relevance feedback extensively across multiple retrieval domains, languages, and modalities, while also investigating aspects such as the performance and latency implications of the number of distillation updates and feedback candidates. |
Revanth Gangi Reddy; Pradeep Dasigi; Md Arafat Sultan; Arman Cohan; Avirup Sil; Heng Ji; Hannaneh Hajishirzi; |
| 330 | KGMEL: Knowledge Graph-Enhanced Multimodal Entity Linking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose KGMEL, a novel framework that leverages KG triples to enhance MEL. |
Juyeon Kim; Geon Lee; Taeuk Kim; Kijung Shin; |
| 331 | HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent studies have attempted to integrate user-side heterogeneous features into item representation sequences, but item-side heterogeneous features, which are vital for performance, remain excluded. To address these challenges, we propose a Heterogeneous Information Transformer model for Sequential Recommendation (HeterRec), which incorporates Heterogeneous Token Flatten Layer (HTFL) and Hierarchical Causal Transformer Layer (HCT). |
Hao Deng; Haibo Xing; Kanefumi Matsuyama; Yulei Huang; Jinxin Hu; Hong Wen; Jia Xu; Zulong Chen; Yu Zhang; Xiaoyi Zeng; Jing Zhang; |
| 332 | Understanding Large Language Model Performance in Software Engineering: A Large-scale Question Answering Benchmark Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce CodeRepoQA, a large-scale benchmark specifically designed for evaluating repository-level question-answering capabilities in the field of software engineering. |
Ruida Hu; Chao Peng; Jingyi Ren; Bo Jiang; Xiangxin Meng; Qinyun Wu; Pengfei Gao; Xinchen Wang; Cuiyun Gao; |
| 333 | Towards Principled Learning for Re-ranking in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, principles to guide the learning process of a re-ranker, and to measure the quality of the output of the re-ranker, have been always missing. In this paper, we study such principles to learn a good re-ranker. |
Qunwei Li; Linghui Li; Jianbin Lin; Wenliang Zhong; |
| 334 | LLM-based Query Expansion Fails for Unfamiliar and Ambiguous Queries Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our results reveal that LLM-based QE can significantly degrade the retrieval effectiveness when knowledge in the LLM is insufficient or query ambiguity is high. We introduce a framework for evaluating QE under these conditions, providing insights into the limitations of LLM-based retrieval augmentation. |
Kenya Abe; Kunihiro Takeoka; Makoto P. Kato; Masafumi Oyamada; |
| 335 | Bridging Time Gaps: Temporal Logic Relations for Enhancing Temporal Reasoning in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a novel Temporal Chain of Thought framework(TempCoT) to improve the performance of LLM in temporal reasoning tasks through a three-stage reasoning strategy. |
Xintong Song; Bin Liang; Yang Sun; Chenhua Zhang; Bingbing Wang; Ruifeng Xu; |
| 336 | The Effects of Demographic Instructions on LLM Personas Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Using demographic data from Twitter, we employ large language models (LLMs) to personalize the identification of sexism. |
Angel Felipe Magnoss\~{a}o de Paula; J. Shane Culpepper; Alistair Moffat; Sachin Pathiyan Cherumanal; Falk Scholer; Johanne Trippas; |
| 337 | Augmenting Vision-Language Retrieval: The Role of Multimodal LLMs As Synthetic Data Generators Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper investigates the effects of fine-tuning cross-modal retrieval models using both human-annotated and MLLM-generated captions for artistic paintings. |
Aidan Bell; James Gore; Behrooz Mansouri; |
| 338 | LLM-Driven Usefulness Labeling for IR Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study focuses on LLM-generated usefulness labels, a crucial evaluation metric that considers the user’s search intents and task objectives, an aspect where relevance falls short. |
Mouly Dewan; Jiqun Liu; Chirag Shah; |
| 339 | Permutation-Invariant Transformers for Attribute Embeddings in Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing set embedding methods have primarily focused on domains such as graphs, with limited applicability to NLP tasks. To address this gap, we propose a Phrase-Localized Attention Network (PLAN), a Transformer-based model that ensures intra-attribute order preservation while enabling permutation-invariant representations at the set level. |
Ernest Kirubakaran Selvaraj; Akshay Shah; Shubham Agrawal; Riya Hedaoo; Samira Golsefid; |
| 340 | HAETAE: In-domain Table Pretraining with Header Anchoring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This inconsistency undermines the generalizability of embeddings across in-domain tables that share universal semantics. To address this gap, we propose a novel pretraining method for in-domain tables, HAETAE, that explicitly separates header embeddings from contextual entity embeddings. |
Woojun Jung; Susik Yoon; |
| 341 | RSGEA: Relationship Structure Line Graph for Semi-supervised Entity Alignment Based on Edge Weight Adjustment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, entity enhancement strategies remain underutilized. To address these issues, we propose a novel Relationship Structure Line Graph for Semi-supervised Entity Alignment Based on Edge Weight Adjustment, named RSGEA. |
Linlin Ding; Mengjunyao Si; Mo Li; |
| 342 | HiLTV: Hierarchical Multi-Distribution Modeling for Lifetime Value Prediction in Online Games Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, existing methods fail to capture the multi-modal distribution of LTV values, and suffer from bias when predicting LTV values of games that users have not registered, i.e., new users. To address these challenges, we propose HiLTV, a novel hierarchical framework for LTV prediction in online games. |
Junwei Xu; Aisi Zheng; Ling Ding; Huangbin Zhang; Zhengwei Deng; Qun Yu; Xiao-Ping Zhang; |
| 343 | LLM As User Simulator: Towards Training News Recommender Without Real User Interactions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: News recommendation systems traditionally rely on extensive real user interaction data to personalize content, which is often inaccessible and raises privacy concerns, particularly in regions lacking such data. To address these challenges, we propose LAUS (LLM As User Simulator), a novel framework that leverages LLM to simulate user interactions for training a news recommender without real user data. |
Choongwon Park; |
| 344 | Understanding Audio-Text Retrieval Through Singular Value Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a LAtent Space Embedding Rank (LASER) analysis. |
Yoori Oh; Yoseob Han; Joonhyeon Bae; Jaeheon Sim; Kyogu Lee; |
| 345 | Deep Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To alleviate the discrepancy, we propose the Deep Multiple Quantization Network (DMQN) to process long behavior sequence end-to-end through compressing the long behavior sequence. |
Zhuoxing Wei; Qi Liu; Qingchen Xie; |
| 346 | Leveraging Artificial Intelligence-Powered Virtual Assistant for Information Retrieval in Indigenous Agriculture: Insights from Nigeria Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study examines the use of an AI-powered Virtual Assistant (VA) to aid information retrieval in the cultivation of indigenous vegetables in Nigeria. |
Olusesan Michael Awoleye; Oluwamayowa Adebisi; Idowu Ademola Atoloye; Atanda Samuel Oladejo; Abiodun T. Atoloye; Cornelius Talade Atere; Victoria Tanimonure; |
| 347 | Dense Retrieval for Low Resource Languages – The Case of Amharic Language Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents our investigation into dense retrieval models for Amharic, a low-resource language spoken by more than 120 million people. |
Tilahun Yeshambel; Moncef Garouani; Serge Molina; Josiane Mothe; |
| 348 | Advancing Chichewa IR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we discuss our work on developing language resources and tools for Chichewa. |
Stanley Ndebvu; Reuben Moyo; Catherine Chavula; |
| 349 | Towards Enhanced Agricultural Information Access in Kiswahili: Integrating Knowledge Graphs and Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Access to and consumption of agricultural research findings remains a challenge for Kiswahili-speaking farmers and extension officers in Tanzania due to the predominance of English in agriculture scholarly publications. To address this challenge, the Mkulima repository, a digital collection of over 600 Swahili agricultural publications, was developed at the Sokoine University of Agriculture to provide agriculture knowledge in Kiswahili. |
Joseph P. Telemala; Neema N. Lyimo; Anna R. Kimaro; Camilius A. Sanga; |
| 350 | Some Things Never Change: Overcoming Persistent Challenges in Children IR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, the paucity of IR research on how search and recommender systems serve and/or ultimately affect children translates into one of many ‘Low-resource environments’ in IR. Drawing from the literature and our experience in this area, we highlight key challenges and encourage greater attention from the IR community to address this critical gap. |
Maria Soledad Pera; Theo Huibers; Emiliana Murgia; Monica Landoni; |
| 351 | Small Data, Big Impact: Navigating Resource Limitations in Point-of-Interest Recommendation for Individuals with Autism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, demographic constraints, difficulties in engaging ASD users, and the complexity of obtaining sensory data position POI recommendation for ASD people as a low-resource problem. In this paper, we identify key challenges in developing such systems and present our ongoing efforts. |
Ludovico Boratto; Federica Cena; Mirko Marras; Noemi Mauro; Giacomo Medda; |
| 352 | IR for AAC Users: A Hyperdimensional Computing (Vector Symbolic Architectures) Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes Hyperdimensional Computing (HDC) as a design paradigm [13] to facilitate search and recommendation activities for disabled users employing symbolic augmentative and alternative communication (AAC) systems. |
Hunter Briegel; Maya Pagal; J. Shane Culpepper; |
| 353 | Efficient Approximate Nearest Neighbor Search on A Raspberry Pi Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this proposal, we explore efficient solutions for large-scale ANN search on low-resource devices. |
Silvio Martinico; Franco Maria Nardini; Cosimo Rulli; Rossano Venturini; |
| 354 | When Less Is Enough: Optimizations for Low-Cost Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a comprehensive approach that combines model-side optimizations (e.g., fused operations, reducing system responsiveness) with system-side enhancements (e.g., improved load balancing, half precision) to lower infrastructure costs without significantly compromising user engagement. |
Md Danish Kalim; Deepanshu Hardaha; Shivansh Joshi; |
| 355 | Fair Access to Food Data in Africa: An Approach Based on Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Retrieval-Augmented Generation-based (RAG) approach for fair access to food data in developing countries. |
Jean Petit BIKIM; Charles Loic Njiosseu; Emmanuel Leuna FIENKAK; Azanzi Jiomekong; S\{o}ren Auer; |
| 356 | 2D Matryoshka Training for Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite its success, discrepancies exist between two published implementations, leading to varied comparative results with baseline models. In this reproducibility study, we implement and evaluate both versions of 2D Matryoshka Training on STS tasks and extend our analysis to retrieval tasks. |
Shuai Wang; Shengyao Zhuang; Bevan Koopman; Guido Zuccon; |
| 357 | A Reproducibility Study of Graph-Based Legal Case Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, while this shift in approaching legal case retrieval is a promising direction in an understudied area of graph-based legal IR, challenges in reproducing novel results have recently been highlighted, with multiple studies reporting difficulties in reproducing previous findings. Thus, in this work we reproduce CaseLink, a graph-based legal case retrieval method, to support future research in this area of IR. |
Gregor Donabauer; Udo Kruschwitz; |
| 358 | A Reproducibility Study of LLM Setwise Reranker with Heapsort Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we reproduce the batching and reranking of Zhuang et al. with a larger comparison window size. |
Dawn Lawrie; Efsun Kayi; James Mayfield; Eugene Yang; Andrew Yates; Douglas W. Oard; |
| 359 | A Worrying Reproducibility Study of Intent-Aware Recommendation Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the broader context of complex neural recommendation models, a growing number of research works unfortunately indicates that (i) reproducing such works is often difficult and (ii) that the true benefits of such models may be limited in reality, e.g., because the reported improvements were obtained through comparisons with untuned or weak baselines. In this work, we investigate if recent research in IARS is similarly affected by such problems. |
Faisal Shehzad; Maurizio Ferrari Dacrema; Dietmar Jannach; |
| 360 | Accelerating Listwise Reranking: Reproducing and Enhancing FIRST Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: What’s more, training on the language modeling (LM) objective is not intrinsically aligned with reranking tasks. To address these challenges, FIRST, a novel approach for listwise reranking, integrates a learning-to-rank objective and leverages only the logits of the first generated token for reranking, significantly reducing computational overhead while preserving effectiveness. |
Zijian Chen; Ronak Pradeep; Jimmy Lin; |
| 361 | Assessing Effective Token Length of Multimodal Models for Text-to-Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we build on the Long-CLIP study, and extend the analysis to other widely used multimodal models and find their effective token length. |
Le Nguyen; Preet Jain; Krutik Panchal; Md Tanvirul Alam; Nidhi Rastogi; |
| 362 | Benchmark Granularity and Model Robustness for Image-Text Retrieval: A Reproducibility Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Using both standard benchmarks (MS-COCO, Flickr30k) and their fine-grained variants, we show that richer captions consistently enhance retrieval, especially in text-to-image tasks, where we observe an average improvement of 16.23\%, compared to 6.44\% in image-to-text. To assess robustness, we introduce a taxonomy of perturbations and conduct extensive experiments, revealing that while perturbations typically degrade performance, they can also unexpectedly improve retrieval, exposing nuanced model behaviors. |
Mariya Hendriksen; Shuo Zhang; Ridho Reinanda; Mohamed Yahya; Edgar Meij; Maarten de Rijke; |
| 363 | Benchmarking LLM-based Relevance Judgment Methods Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we systematically compare multiple LLM-based relevance assessment methods, including binary relevance judgments, graded relevance assessments, pairwise preference-based methods, and two nugget-based evaluation methods~-~document-agnostic and document-dependent. |
Negar Arabzadeh; Charles L. A. Clarke; |
| 364 | Beyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study presents a comprehensive reproducibility analysis and extension of the Setwise prompting method for zero-shot ranking with Large Language Models (LLMs), as proposed by Zhuang et al. |
Jakub Podolak; Leon Peri\'{c}; Mina Jani\'{c}ijevi\'{c}; Roxana Petcu; |
| 365 | Does UMBRELA Work on Other LLMs? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We reproduce the UMBRELA LLM Judge evaluation framework across a range of large language models (LLMs) to assess its generalizability beyond the original study. |
Naghmeh Farzi; Laura Dietz; |
| 366 | Gosling Grows Up: Retrieval with Learned Dense and Sparse Representations Using Anserini Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We discuss how Anserini has evolved in response to this changing environment, the most significant of which is the advent of transformer-based retrieval models that did not exist when the project started. |
Jimmy Lin; Arthur Haonan Chen; Carlos Lassance; Xueguang Ma; Ronak Pradeep; Tommaso Teofili; Jasper Xian; Jheng-Hong Yang; Brayden Zhong; Vincent Zhong; |
| 367 | Information Leakage of Sentence Embeddings Via Generative Embedding Inversion Attacks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we reproduce GEIA’s findings across various neural sentence embedding models. |
Antonios Tragoudaras; Theofanis Aslanidis; Emmanouil Georgios Lionis; Marina Orozco Gonz\'{a}lez; Panagiotis Eustratiadis; |
| 368 | Inside Out 2: Make Room for New Emotions \& LLM: A Reproducibility Study of The Emotional Side of Search in The Classroom Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we conduct a comprehensive reproducibility study where we probe today’s emotional profile of SE using both a lexicon-based and a language-model based approach tailored to the Italian language, thus addressing an acknowledged limitation of the original study. |
Hrishita Chakrabarti; Diletta Micol Tobia; Monica Landoni; Maria Soledad Pera; |
| 369 | Interpreting Multilingual and Document-Length Sensitive Relevance Computations in Neural Retrieval Models Through Axiomatic Causal Interventions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This reproducibility study analyzes and extends the paper ”Ax- iomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models,” which investigates how neural retrieval models encode task-relevant properties such as term frequency. |
Oliver Savolainen; Dur e Najaf Amjad; Roxana Petcu; |
| 370 | Investigating The Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, many previous simulation-based experiments are somewhat limited because they may have one or more of the following deficiencies: 1) using a weak production ranker to generate initial ranked lists, 2) relying on a simplified user simulation model to simulate user clicks, and 3) generating a fixed number of synthetic click logs. As a result, the robustness of CLTR models in complex and diverse situations is largely unknown and needs further investigation.To address this problem, in this paper, we aim to investigate the robustness of existing CLTR models in a reproducibility study with extensive simulation-based experiments that (1) use production rankers with different ranking performance (2) leverage multiple user simulation models with different user behavior assumptions, and (3) generate different numbers of synthetic sessions for the training queries. |
Zechun Niu; Zhilin Zhang; Jiaxin Mao; Qingyao Ai; Ji-Rong Wen; |
| 371 | Pre-training Vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our study confirms that in DPR tuning, pre-trained knowledge underpins retrieval performance, with fine-tuning primarily adjusting neuron activation rather than reorganizing knowledge. |
Zheng Yao; Shuai Wang; Guido Zuccon; |
| 372 | RARR Unraveled: Component-Level Insights Into Hallucination Detection and Mitigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The Retrofit Attribution using Research and Revision (RARR) framework addresses this challenge by extracting key aspects of an LLM response, verifying them against retrieved evidence, and resolving errors through re-prompting. In this work, we critically examine RARR and adapt its framework to incorporate publicly available evidence retrieval systems and generative models, thereby operationalizing the approach. |
Jonathan J Ross; Ekaterina Khramtsova; Anton van der Vegt; Bevan Koopman; Guido Zuccon; |
| 373 | Reassessing Large Language Model Boolean Query Generation for Systematic Reviews Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, its findings diverged significantly from the original study. In this work, we systematically reproduce both studies while addressing these overlooked factors. |
Shuai Wang; Harrisen Scells; Bevan Koopman; Guido Zuccon; |
| 374 | Reassessing The Effectiveness of Reinforcement Learning Based Recommender Systems for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A recent study suggests that reported performance gains of combining RL with supervised learning techniques, as done in the Self-Supervised Q-Learning (SQN) framework, may actually not come from learning an optimal policy, but that the RL component helps to learn embeddings that encode the users’ past interactions. Given these observations, we aimed to reassess the performance of RL-enhanced sequential recommendations in the SQN framework. |
Dilina Chandika Rajapakse; Dietmar Jannach; |
| 375 | Refined Medical Search Via Dense Retrieval and User Interaction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we reimplement and reproduce the log-augmented dense retrieval approach introduced by Jin, Shin, and Lu in 2023. |
Reyhaneh Goli; Alistair Moffat; George Buchanan; |
| 376 | Replication and Exploration of Generative Retrieval Over Dynamic Corpora Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Through extensive experiments, we reveal that existing GR models with text-based docids show superior generalization to unseen documents. We observe that the more fine-grained the docid design in the GR model, the better its performance over dynamic corpora, surpassing BM25 and even being comparable to dense retrieval methods. |
Zhen Zhang; Xinyu Ma; Weiwei Sun; Pengjie Ren; Zhumin Chen; Shuaiqiang Wang; Dawei Yin; Maarten de Rijke; Zhaochun Ren; |
| 377 | Reproducibility, Replicability, and Insights Into Visual Document Retrieval with Late Interaction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we investigate the reproducibility and replicability of VDR methods with and without late interaction mechanisms by systematically evaluating their performance across multiple pre-trained vision-language models. |
Jingfen Qiao; Jia-Huei Ju; Xinyu Ma; Evangelos Kanoulas; Andrew Yates; |
| 378 | Reproducing NevIR: Negation in Neural Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we reproduce and extend the findings of NevIR, a benchmark study that revealed most IR models perform at or below the level of random ranking when dealing with negation. |
Coen van den Elsen; Francien Barkhof; Thijmen Nijdam; Simon Lupart; Mohammad Aliannejadi; |
| 379 | Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study the reproducibility of the existing sockpuppeting audits of TikTok recommender systems, and the generalizability of their findings. |
Matej Mosnar; Adam Skurla; Branislav Pecher; Matus Tibensky; Jan Jakubcik; Adrian Bindas; Peter Sakalik; Ivan Srba; |
| 380 | Unveiling DIME: Reproducibility, Generalizability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose four key contributions. First, we provide a rigorous theoretical analysis of DIME, framing it as a denoising mechanism that mitigates embedding noise while preserving the salient information. |
Cesare Campagnano; Antonio Mallia; Fabrizio Silvestri; |
| 381 | Unlearning for Federated Online Learning to Rank: A Reproducibility Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper reports on findings from a comparative study on the effectiveness and efficiency of federated unlearning strategies within Federated Online Learning to Rank (FOLTR), with specific attention to systematically analysing the unlearning capabilities of methods in a verifiable manner. |
Yiling Tao; Shuyi Wang; Jiaxi Yang; Guido Zuccon; |
| 382 | Variations in Relevance Judgments and The Shelf Life of Test Collections Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Under these changes, it is unclear whether assessor disagreement remains negligible for system comparisons. We investigate this aspect under the additional condition that the few modern test collections are heavily re-used. |
Andrew Parry; Maik Fr\{o}be; Harrisen Scells; Ferdinand Schlatt; Guglielmo Faggioli; Saber Zerhoudi; Sean MacAvaney; Eugene Yang; |
| 383 | Tip of The Tongue Query Elicitation for Simulated Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Research on TOT retrieval is further constrained by the challenge of collecting queries, as current approaches rely heavily on community question-answering (CQA) websites, leading to labor-intensive evaluation and domain bias. To overcome these limitations, we introduce two methods for eliciting TOT queries-leveraging large language models (LLMs) and human participants-to facilitate simulated evaluations of TOT retrieval systems. |
Yifan He; To Eun Kim; Fernando Diaz; Jaime Arguello; Bhaskar Mitra; |
| 384 | A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We describe rbstar a toolkit of software for carrying out measurements when the goal is to determine how similar a system observation is to a gold-standard reference output. |
Alistair Moffat; Joel Mackenzie; Antonio Mallia; Matthias Petri; |
| 385 | A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we provide an overview of the dataset and baseline experiments (classification tasks) that can inspire researchers about the different possibilities for future work. |
Kayhan Latifzadeh; Jacek Gwizdka; Luis A. Leiva; |
| 386 | An EEG Dataset of Word-level Brain Responses for Semantic Text Relevance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given that information retrieval research depends on understanding and modelling relevance, we present a novel dataset including EEG data recorded while participants read text that is semantically relevant or irrelevant to self-selected topics. |
Vadym Gryshchuk; Michiel M. Spap\'{e}; Maria Maistro; Christina Lioma; Tuukka Ruotsalo; |
| 387 | Benchmarking Recommendation, Classification, and Tracing Based on Hugging Face Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing platforms like Hugging Face do not explicitly utilize structured representations, limiting advanced queries and analyses such as tracing model evolution and recommending relevant datasets. To fill the gap, we construct HuggingKG, the first large-scale knowledge graph built from the Hugging Face community for ML resource management. |
Qiaosheng Chen; Kaijia Huang; Xiao Zhou; Weiqing Luo; Yuanning Cui; Gong Cheng; |
| 388 | CoLoTa: A Dataset for Entity-based Commonsense Reasoning Over Long-Tail Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we observe that even the most prominent LLMs, such as OpenAI-o1, suffer from high rates of reasoning errors and hallucinations on tasks requiring commonsense reasoning over obscure, long-tail entities. |
Armin Toroghi; Willis Guo; Scott Sanner; |
| 389 | Conversational Gold: Evaluating Personalized Conversational Search System Using Gold Nuggets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a new resource for assessing the retrieval effectiveness and relevance of responses generated by RAG systems, using a nugget-based evaluation framework. |
Zahra Abbasiantaeb; Simon Lupart; Leif Azzopardi; Jeffrey Dalton; Mohammad Aliannejadi; |
| 390 | CoSRec: A Joint Conversational Search and Recommendation Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As of today, the scarce availability of integrated datasets – focused exclusively on either of the tasks – limits the possibilities for evaluating by-design integrated CS and CR systems. To address this gap, we propose CoSRec, the first dataset for joint Conversational Search and Recommendation (CSR) evaluation. |
Marco Alessio; Simone Merlo; Tommaso Di Noia; Guglielmo Faggioli; Marco Ferrante; Nicola Ferro; Cristina Ioana Muntean; Franco Maria Nardini; Fedelucio Narducci; Raffaele Perego; Giuseppe Santucci; Nicola Viterbo; |
| 391 | DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A primary obstacle lies in the fragmented and often opaque data management strategies employed during the preprocessing stage, where decisions about dataset selection, filtering, and splitting can substantially influence outcomes. To address these limitations, we introduce DataRec, an open-source Python-based library specifically designed to unify and streamline data handling in recommender system research. |
Alberto Carlo Maria Mancino; Salvatore Bufi; Angela Di Fazio; Antonio Ferrara; Daniele Malitesta; Claudio Pomo; Tommaso Di Noia; |
| 392 | Doctron: A Web-based Collaborative Annotation Tool for Ground Truth Creation in IR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce Doctron, a web-based, dockerized annotation tool designed to streamline ground truth creation for IR tasks. |
Ornella Irrera; Stefano Marchesin; Farzad Shami; Gianmaria Silvello; |
| 393 | Dynamic-KGQA: A Scalable Framework for Generating Adaptive Question Answering Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce Dynamic-KGQA, a scalable framework for generating adaptive QA datasets from knowledge graphs (KGs), designed to mitigate memorization risks while maintaining statistical consistency across iterations. |
Preetam Prabhu Srikar Dammu; Himanshu Naidu; Chirag Shah; |
| 394 | ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a guided hallucination-based approach ELOQ . |
Zhiyuan Peng; Jinming Nian; Alexandre Evfimievski; Yi Fang; |
| 395 | Extending MovieLens-32M to Provide New Evaluation Objectives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our primary objective is to predict the movies that a user would be interested in watching, i.e. predict their watchlist. |
Mark D. Smucker; Houmaan Chamani; |
| 396 | FACTors: A New Dataset for Studying The Fact-checking Ecosystem Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, to the best of our knowledge, there is no fact-checking dataset at the ecosystem level, covering claims from a sufficiently long period of time and sourced from a wide range of actors reflecting the entire ecosystem that admittedly follows widely-accepted codes and principles of fact-checking. We present a new dataset FACTors, the first to fill this gap by presenting ecosystem-level data on fact-checking. |
Enes Altuncu; Can Baskent; Sanjay Bhattacherjee; Shujun Li; Dwaipayan Roy; |
| 397 | FairDiverse: A Comprehensive Toolkit for Fairness- and Diversity-aware Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, there is an urgent need for a comprehensive IR toolkit, enabling standardized assessments of fairness- and diversity-aware algorithms across IR tasks. To address these issues, we introduce an open-source standardized toolkit called FairDiverse. |
Chen Xu; Zhirui Deng; Clara Rus; Xiaopeng Ye; Yuanna Liu; Jun Xu; Zhicheng Dou; Ji-Rong Wen; Maarten de Rijke; |
| 398 | IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper addresses the challenges of developing interactive agents capable of understanding and executing grounded natural language instructions through the IGLU competition. |
Shrestha Mohanty; Negar Arabzadeh; Andrea Tupini; Yuxuan Sun; Alexey Skrynnik; Artem Zholus; Marc-Alexandre C\^{o}t\'{e}; Julia Kiseleva; |
| 399 | Ir_explain: A Python Library of Explainable IR Methods Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present ir_explain, an open-source Python library that implements a variety of well-known techniques for Explainable IR (ExIR) within a common, extensible framework. |
Sourav Saha; Harsh Agarwal; Venktesh V; Avishek Anand; Swastik Mohanty; Debapriyo Majumdar; Mandar Mitra; |
| 400 | JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces JuDGE (Judgment Document Generation Evaluation), a novel benchmark for evaluating the performance of judgment document generation in the Chinese legal system. |
Weihang Su; Baoqing Yue; Qingyao Ai; Yiran Hu; Jiaqi Li; Changyue Wang; Kaiyuan Zhang; Yueyue Wu; Yiqun Liu; |
| 401 | KIMERA: From Evaluation-as-a-Service to Evaluation-in-the-Cloud Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Kubernetes Infrastructure for Managed Evaluation and Resource Access (KIMERA) as the next step from EaaS into Evaluation-in-the-Cloud (EitC), allowing researchers to directly code and execute their systems through their browsers, requiring only an internet connection. |
Andrea Pasin; Nicola Ferro; |
| 402 | LEMSS: LLM-Based Platform for Multi-Agent Competitive Search Simulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce LEMSS: a multi-agent platform that leverages LLMs as publishers in competitive search settings. |
Tommy Mordo; Tomer Kordonsky; Haya Nachimovsky; Moshe Tennenholtz; Oren Kurland; |
| 403 | LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, there is no benchmark specifically designed to assess the effectiveness of RAG in the legal domain, which restricts progress in this area. To fill this gap, we propose LexRAG, the first benchmark to evaluate RAG systems for multi-turn legal consultations. |
Haitao Li; Yifan Chen; Hu YiRan; Qingyao Ai; Junjie Chen; Xiaoyu Yang; Jianhui Yang; Yueyue Wu; Zeyang Liu; Yiqun Liu; |
| 404 | MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing RAG methods primarily focus on generating text-only answers, even in Multimodal Retrieval-Augmented Generation (MRAG) scenarios, where multimodal elements are retrieved to assist in generating text answers. To address this, we introduce the Multimodal Retrieval-Augmented Multimodal Generation (MRAMG) task, in which we aim to generate multimodal answers that combine both text and images, fully leveraging the multimodal data within a corpus. |
Qinhan Yu; Zhiyou Xiao; Binghui Li; Zhengren Wang; Chong Chen; Wentao Zhang; |
| 405 | MultiConAD: A Unified Multilingual Conversational Dataset for Early Alzheimer’s Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study highlights the challenges in multilingual AD detection and enables future research on both language-specific approaches and techniques aimed at improving model generalization and robustness. |
Arezo Shakeri; Mina Farmanbar; Krisztian Balog; |
| 406 | NlcTables: A Dataset for Marrying Natural Language Conditions with Table Discovery Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing table discovery methods primarily retrieve desired tables based on a query table or several vague keywords, leaving users to manually filter large result sets. To address this limitation, we propose a new task: NL-conditional table discovery (nlcTD), where users combine a query table with natural language (NL) requirements to refine search results. |
Lingxi Cui; Huan Li; Ke Chen; Lidan Shou; Gang Chen; |
| 407 | PILs of Knowledge: A Synthetic Benchmark for Evaluating Question Answering Systems in Healthcare Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, no dedicated benchmark currently exists to evaluate QA systems specifically on PILs, limiting progress in this domain. To address this gap, we introduce a fact-supported synthetic benchmark composed of multiple-choice questions and answers generated from real PILs. |
Riccardo Lunardi; Michael Soprano; Paolo Coppola; Vincenzo Della Mea; Stefano Mizzaro; Kevin Roitero; |
| 408 | PSCon: Product Search Through Conversations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a CPS data collection protocol and create a new CPS dataset, called PSCon, which assists product search through conversations with human-like language. |
Jie Zou; Mohammad Aliannejadi; Evangelos Kanoulas; Shuxi Han; Heli Ma; Zheng Wang; Yang Yang; Heng Tao Shen; |
| 409 | Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the growing need for developing better S&R services, we present a novel multimodal information retrieval dataset in this paper, namely Qilin. |
Jia Chen; Qian Dong; Haitao Li; Xiaohui He; Yan Gao; Shaosheng Cao; Yi Wu; Ping Yang; Chen Xu; Yao Hu; Qingyao Ai; Yiqun Liu; |
| 410 | RankLLM: A Python Package for Reranking with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces RankLLM, an open-source Python package for reranking that is modular, highly configurable, and supports both proprietary and open-source LLMs in customized reranking workflows. |
Sahel Sharifymoghaddam; Ronak Pradeep; Andre Slavescu; Ryan Nguyen; Andrew Xu; Zijian Chen; Yilin Zhang; Yidi Chen; Jasper Xian; Jimmy Lin; |
| 411 | REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Retrieval test collections are essential for evaluating information retrieval systems, yet they often lack generalizability across tasks. To overcome this limitation, we introduce REANIMATOR, a versatile framework designed to enable the repurposing of existing test collections by enriching them with extracted and synthetic resources. |
Bj\{o}rn Engelmann; Fabian Haak; Philipp Schaer; Mani Erfanian Abdoust; Linus Netze; Meik Bittkowski; |
| 412 | RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce the RecGaze dataset: the first comprehensive feedback dataset on carousels that includes eye tracking results, clicks, cursor movements, and selection explanations. |
Santiago de Leon-Martinez; Jingwei Kang; Robert Moro; Maarten de Rijke; Branislav Kveton; Harrie Oosterhuis; Maria Bielikova; |
| 413 | Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for Deep Research Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Researchy Questions, the world’s first, only and largest public dataset of ”Deep Research” questions filtered from real search engine logs to be non-factoid, ”decompositional” and multi-perspective. |
Corbin Rosset; Ho-Lam Chung; Guanghui Qin; Ethan Chau; Zhuo Feng; Ahmed Awadallah; Jennifer Neville; Nikhil Rao; |
| 414 | Resource for Error Analysis in Text Simplification: New Taxonomy and Test Collection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This resource paper addresses this gap by introducing a test collection for detecting and classifying errors in simplified texts. |
Benjamin Vendeville; Liana Ermakova; Pierre De Loor; |
| 415 | SAGraph: A Large-Scale Social Graph Dataset with Comprehensive Context for Influencer Selection in Marketing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this reductionist approach fails to capture the dynamic nature of influencer marketing effectiveness. To bridge this gap, we present SAGraph, a novel comprehensive dataset from Weibo that captures multi-dimensional marketing campaign data across six product domains. |
Xiaoqing Zhang; Yuhan Liu; Jianzhou Wang; Zhenxing Hu; Xiuying Chen; Rui Yan; |
| 416 | SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Data Generation for Personalized Tourism Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore using Large Language Models (LLMs) to generate synthetic travel queries that emulate diverse user personas and incorporate structured filters such as budget constraints and sustainability preferences. |
Ashmi Banerjee; Adithi Satish; Fitri Nur Aisyah; Wolfgang W\{o}rndl; Yashar Deldjoo; |
| 417 | TAFSIL: Taxonomy Adaptable Fine-grained Entity Recognition Through Distant Supervision for Indian Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces TAFSIL, a taxonomy-adaptable FgER framework to create FgER datasets in six Indian languages. |
Prachuryya Kaushik; Shivansh Mishra; Ashish Anand; |
| 418 | TIREx Tracker: The Information Retrieval Experiment Tracker Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Expanding ir_metadata, we present the TIREx tracker, a tool that records hardware configurations, power/CPU/RAM/GPU usage, and experiment/system versions. |
Tim Hagen; Maik Fr\{o}be; Jan Heinrich Merker; Harrisen Scells; Matthias Hagen; Martin Potthast; |
| 419 | U-Sticker: A Large-Scale Multi-Domain User Sticker Dataset for Retrieval and Personalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, a major limitation in existing literature has been the lack of datasets capturing temporal and user-specific sticker interactions, which has hindered further progress in user modeling and sticker personalization. To address this, we introduce User-Sticker, a dataset that includes temporal and user anonymous ID across conversations. |
Heng Er Metilda Chee; Jiayin Wang; Zhiqiang Guo; Weizhi Ma; Qinglang Guo; Min Zhang; |
| 420 | LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose LUMA, a unique multimodal dataset, featuring audio, image, and textual data from 50 classes, specifically designed for learning from uncertain data. |
Grigor Bezirganyan; Sana Sellami; Laure Berti-\'{E}quille; S\'{e}bastien Fournier; |
| 421 | WebClasSeg-25: A Dual-Classified Webpage Segmentation Dataset – Integrating Functional and Maturity-Based Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing segmentation datasets often lack both comprehensive visual and textual segmentation, as well as classification systems that capture the functional and qualitative aspects of webpages. In this paper, we introduce a novel webpage segmentation dataset that addresses these gaps by providing both visual and textual segmentations, alongside two classification frameworks. |
Jonathan Gerber; Jasmin Saxer; Kimia Rabishokr; Bruno Kreiner; Andreas Weiler; |
| 422 | WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from FAQ-style schema.org annotations. |
Michael Dinzinger; Laura Caspari; Kanishka Ghosh Dastidar; Jelena Mitrovi\'{c}; Michael Granitzer; |
| 423 | Wiki-TabNER: Integrating Named Entity Recognition Into Wikipedia Tables Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we describe the distinguishing features of the Wiki-TabNER dataset and the labeling process. |
Aneta Koleva; Martin Ringsquandl; Ahmed Hatem; Thomas Runkler; Volker Tresp; |
| 424 | WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In the time when information is readily accessible, it is crucial to stimulate and preserve human cognitive abilities and maintain strong reasoning skills. This paper addresses such challenges by promoting the use of hints as an alternative or a supplement to direct answers. |
Jamshid Mozafari; Florian Gerhold; Adam Jatowt; |
| 425 | Wrong Answers Can Also Be Useful: PlausibleQA – A Large-Scale QA Dataset with Answer Plausibility Scores Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing QA datasets primarily focus on correct answers without explicit consideration of the plausibility of other candidate answers, limiting opportunity for more nuanced evaluations of models. To address this gap, we introduce PlausibleQA, a large-scale dataset comprising 10,000 questions and 100,000 candidate answers, each annotated with plausibility scores and justifications for their selection. |
Jamshid Mozafari; Abdelrahman Abdallah; Bhawna Piryani; Adam Jatowt; |
| 426 | An Instruction-Response Perspective on Large Language Models in Information Retrieval Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The lack of transparency in LLMs means that even subtle variations in instructions can significantly impact the quality, consistency, and reliability of their responses. To address this issue, we propose Instruction-Response Study, an experimental framework for systematically analysing how task instructions influence LLM-generated responses in IR tasks. |
Hideo Joho; Joemon M Jose; |
| 427 | From Query to Conscience: The Importance of Information Retrieval in Empowering Socially Responsible Consumerism Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this perspectives paper, we argue that the field of Information Retrieval (IR) has a critical role to play by empowering consumers to make more informed and more responsible choices. |
Frans van der Sluis; Leif Azzopardi; Florian Meier; |
| 428 | Rankers, Judges, and Assistants: Towards Understanding The Interplay of LLMs in Information Retrieval Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we offer initial guidelines and a research agenda to ensure the reliable use of LLMs in IR evaluation. |
Krisztian Balog; Don Metzler; Zhen Qin; |
| 429 | Information Retrieval for Artificial General Intelligence: A New Perspective of Information Retrieval Research Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a new perspective on IR research in which the users of an IR system are intelligent agents instead of human users. |
ChengXiang Zhai; |
| 430 | Brain-Machine Interfaces \& Information Retrieval Challenges and Opportunities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For each vertex, we identify specific research opportunities and propose concrete directions for developing BMI-enhanced IR systems. |
Yashar Moshfeghi; Niall Mcguire; |
| 431 | Adaptive Orchestration of Modular Generative Information Access Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this perspective paper, we argue that the architecture of future modular generative information access systems will not just assemble powerful components, but enable a self-organizing system through real-time adaptive orchestration – where components’ interactions are dynamically configured for each user input, maximizing information relevance while minimizing computational overhead. |
Mohanna Hoveyda; Harrie Oosterhuis; Arjen P. de Vries; Maarten de Rijke; Faegheh Hasibi; |
| 432 | From To-Do to Ta-Da: Transforming Task-Focused IR with Generative AI Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: GenAI presents an unprecedented opportunity to finally realize the potential of tasks in IR, enhance task-focused retrieval and interaction, and create ”magical” task completion moments for users. In this paper, we explore the rationale and methodology behind this argument. |
Chirag Shah; Ryen W. White; |
| 433 | NExT-Search: Rebuilding User Feedback Ecosystem for Generative AI Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This introduces a feedback loop disconnect, where user feedback for the final output cannot be effectively mapped back to specific system components, making it difficult to improve each intermediate stage and sustain the feedback loop.To address this limitation, we envision NExT-Search, a next-generation paradigm designed to reintroduce fine-grained, process-level feedback into generative AI search. |
Sunhao Dai; Wenjie Wang; Liang Pang; Jun Xu; See-Kiong Ng; Ji-Rong Wen; Tat-Seng Chua; |
| 434 | Toward Holistic Evaluation of Recommender Systems Powered By Generative Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our goal is to provide a guiding framework so that researchers and practitioners can thoroughly assess Gen-RecSys, ensuring both effective personalization and responsible deployment. |
Yashar Deldjoo; Nikhil Mehta; Maheswaran Sathiamoorthy; Shuai Zhang; Pablo Castells; Julian McAuley; |
| 435 | My System Is As Effective As Yours: Reproducibility, Sustainability, and More Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that the equivalence test, which has been gaining popularity in the medical domain for comparing new drugs with standard drugs, is applicable to IR research in situations such as above. |
Tetsuya Sakai; |
| 436 | RelEx: An XAI-Enhanced Relevance Feedback Model for User-Adaptive Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present an XAI-driven extension to the classic relevance feedback model in IR, incorporating user feedback in the process of explaining the model behavior to the user. |
Sayantan Polley; Govind Shukla; Pritha Ghosal; Andreas N\{u}rnberger; |
| 437 | AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents (i) a flexible framework for using LLMs in systematic review tasks, especially title and abstract screening, and (ii) a web-based interface for LLM-assisted screening. |
Xinyu Mao; Teerapong Leelanupab; Martin Potthast; Harrisen Scells; Guido Zuccon; |
| 438 | FairWork: A Generic Framework For Evaluating Fairness In LLM-Based Job Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce FairWork, a comprehensive fairness evaluation framework to examine LLM-based recommender system from both the user’s and recruiter’s perspectives, employing fairness metrics to assess how sensitive user attributes influence job recommendations. |
Yuhan Hu; Ziyu Lyu; Lu Bai; Lixin Cui; |
| 439 | DeepReport: An AI-assisted Idea Generation System for Scientific Research Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Technically, DeepReport maintains evolving concept co-occurrence graphs to extract core insights from over 260 million publications across all disciplines. |
Yi Xu; Luoyi Fu; Shuqian Sheng; Bo Xue; Jiaxin Ding; Lei Zhou; Xinbing Wang; Chenghu Zhou; |
| 440 | Artifact Sharing for Information Retrieval Research Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given the practical utility of using shared indexes, researchers have resorted to self-hosting these resources or performing ad hoc file transfers upon request, ultimately limiting the artifacts’ discoverability and reuse. This demonstration introduces a flexible and interoperable way to share artifacts for Information Retrieval research, improving both their accessibility and usability. |
Sean MacAvaney; |
| 441 | OnSET: Ontology and Semantic Exploration Toolkit Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel system, Ontology and Semantic Exploration Toolkit (OnSET), that allows novice users to quickly build queries with visual user guidance provided by topic modeling and semantic search throughout the application. |
Benedikt Kantz; Kevin Innerebner; Peter Waldert; Stefan Lengauer; Elisabeth Lex; Tobias Schreck; |
| 442 | Fact Verification in Knowledge Graphs Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents FactCheck, a fact verification system topped by a web platform that shows how Large Language Models (LLMs) can be collectively used to verify facts within Knowledge Graphs (KGs). |
Farzad Shami; Stefano Marchesin; Gianmaria Silvello; |
| 443 | NLQxform-UI: An Interactive and Intuitive Scholarly Question Answering System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we develop an interactive and intuitive scholarly question answering system called NLQxform-UI, which allows users to pose complex queries in the form of natural language questions. |
Ruijie Wang; Zhiruo Zhang; Luca Rossetto; Florian Ruosch; Abraham Bernstein; |
| 444 | ROKSANA: An Open-Source Toolkit for Robust Graph-Based Keyword Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce ROKSANA, an open-source Python toolkit designed to support research in graph-based keyword search under adversarial settings. |
Radin Hamidi Rad; Amir Khosrojerdi; Ebrahim Bagheri; |
| 445 | Nugget-based Annotation Protocol and Tool For Evaluating Long-form Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present an annotation protocol and tool tailored to collecting information for evaluating RAG systems. |
Eugene Yang; Dawn Lawrie; Hoa Dang; Ian Soboroff; James Mayfield; |
| 446 | MMMORRF: Multimodal Multilingual MOdularized Reciprocal Rank Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Retrieval benchmarks further reinforce this bias by focusing on visual queries and neglecting other modalities. We create a search system MMMORRF that extracts text and features from both visual and audio modalities and integrates them with a novel modality-aware weighted reciprocal rank fusion. |
Saron Samuel; Dan DeGenaro; Jimena Guallar-Blasco; Kate Sanders; Seun Eisape; Arun Reddy; Alexander Martin; Andrew Yates; Eugene Yang; Cameron Carpenter; David Etter; Efsun Kayi; Matthew Wiesner; Kenton Murray; Reno Kriz; |
| 447 | Advancing Scientific Knowledge Retrieval and Reuse with A Novel Digital Library for Machine-Readable Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present ORKG reborn, an emerging digital library that supports finding, accessing, and reusing accurate, fine-grained, and reproducible machine-readable expressions of scientific knowledge that relate scientific statements and their supporting evidence in terms of data and code. |
Hadi Ghaemi; Lauren Snyder; Markus Stocker; |
| 448 | Conversational Bibliographic Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The system presents a novel user interface and bridges the gap between keyword-based search engines, faceted search systems, and generative conversational approaches. |
Markus Nilles; Ralf Schenkel; |
| 449 | Navigating Speech Recording Collections with AI-Generated Illustrations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel navigational method for speech archives that leverages recent advances in language and multimodal generative models. |
Sirina H\r{a}land; Trond Karlsen Str\o{}m; Petra Galu\v{s}\v{c}\'{a}kov\'{a}; |
| 450 | Combating Biomedical Misinformation Through Multi-modal Claim Detection and Evidence-based Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We developed CER, a comprehensive fact-checking system designed specifically for biomedical content. |
Mariano Barone; Antonio Romano; Giuseppe Riccio; Marco Postiglione; Vincenzo Moscato; |
| 451 | Multimodal Search in Chemical Documents and Reactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a multimodal search tool for retrieval of chemical reactions, molecular structures, and associated text from scientific literature. |
Ayush Kumar Shah; Abhisek Dey; Leo Luo; Bryan Amador; Patrick Philippy; Ming Zhong; Siru Ouyang; David Mark Friday; David Bianchi; Nick Jackson; Richard Zanibbi; Jiawei Han; |
| 452 | Constructing and Evaluating Declarative RAG Pipelines in PyTerrier Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Retrieval augmented generation (RAG) is an exciting application of the pipeline architecture, where the final component generates a coherent answer for the users from the retrieved documents. In this demo paper, we describe how such RAG pipelines can be formulated in the declarative PyTerrier architecture, and the advantages of doing so. |
Craig Macdonald; Jinyuan Fang; Andrew Parry; Zaiqiao Meng; |
| 453 | Targeted Multi-Modal Passage Search for Molecules and Their Synthesis Pathways Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a chemical extraction and search pipeline intended to support information tasks related to drug discovery. |
Abhisek Dey; Nathaniel H. Stanley; Richard Zanibbi; |
| 454 | InstInfo: A Just-in-Time Literature Recommendation System for Presentations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes InstInfo: a novel just-in-time literature recommendation system for presentations. |
Kevin Ros; Rahul Suresh; ChengXiang Zhai; |
| 455 | CoachGPT: A Scaffolding-based Academic Writing Assistant Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large language models (LLMs) have shown remarkable capabilities in generating responses in natural languages based on given prompts, but they have a fundamental limitation in education: they generate essays without teaching, which can have detrimental effects on learning when misused. To address this limitation, we develop CoachGPT, which leverages LLMs to assist academic writing for those with limited educational resources and those who prefer self-paced learning. |
Fumian Chen; Sotheara Veng; Joshua Wilson; Xiaoming Li; Hui Fang; |
| 456 | TINK: Text Information Navigation Kit Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We discuss the modular and extensible two-part structure of TINK. In this demonstration paper we discuss this novel system, its strengths and its weaknesses. |
Dean E. Alvarez; ChengXiang Zhai; |
| 457 | Tevatron 2.0: Unified Document Retrieval Toolkit Across Scale, Language, and Modality Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In response, we have updated the Tevatron toolkit, introducing a unified pipeline that enables researchers to explore retriever models at different scales, across multiple languages, and with various modalities. |
Xueguang Ma; Luyu Gao; Shengyao Zhuang; Jiaqi Samantha Zhan; Jamie Callan; Jimmy Lin; |
| 458 | A Flexible User Study Platform for Generative Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We developed a comprehensive platform to collect user behavior and feedback on the generative IR system. |
Yidong Liang; Zhijing Wu; Yuchen He; Fengming Liang; Kexin Liu; Jiaxin Mao; |
| 459 | ReviewHQ: An API-Based System for Reviewer Assignment and Quality Control in Research Conferences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Managing the review process for large-scale academic conferences poses significant challenges in effectively matching papers to the right reviewers, detecting conflicts of interest, ensuring review quality, and addressing potential ethical issues such as dual submissions. In this demonstration paper, we introduce ReviewHQ, an API-based system designed to streamline conference management. |
Guido Zuccon; |
| 460 | NodeRec+: A Lightweight Framework for Federated Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce NodeRec+, the prototype of an FL framework, geared towards enabling the design and evaluation of decentralised RS. |
Diarmuid O’Reilly-Morgan; Erika Duriakova; Elias Tragos; Neil Hurley; Aonghus Lawlor; |
| 461 | ClusterChat: Multi-Feature Search for Corpus Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present ClusterChat. The demo video and source code are available at: https://github.com/achouhan93/ClusterChat, an open-source system for corpus exploration that integrates cluster-based organization of documents using textual embeddings with lexical and semantic search, timeline-driven exploration, and corpus and document-level question answering (QA) as multi-feature search capabilities. |
Ashish Chouhan; Saifeldin Mandour; Michael Gertz; |
| 462 | A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, re-ranking is faced with the challenges of time complexity and diversity. In light of this, we propose a novel end-to-end generative re-ranking model named Sequential Ordered Regression Transformer-Generator (SORT-Gen) for the less-studied list-level multi-objective optimization problem. |
Yue Meng; Cheng Guo; Yi Cao; Tong Liu; Bo Zheng; |
| 463 | Graph Isomorphism Network-Based Cohort Modeling In Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods have shortcomings in terms of simplistic encoding techniques for warm user behaviors and direct utilization of virtual behavior embeddings, leading to limitations in user interest expression and generalization. To address these challenges, we propose a novel method that leverages Graph Isomorphism Networks (GIN) for cohort modeling within CTR prediction. |
Xuan Ma; Hao Peng; Jia Duan; Zhanhao Ye; Langlang Ye; Zehua Zhang; Jie He; Changping Peng; Zhangang Lin; |
| 464 | MO-LightGBM: A Library for Multi-objective Learning to Rank with LightGBM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces MO-LightGBM, an open-source library built upon LightGBM, specifically designed to offer an integrated, versatile, and easily adaptable framework for Multi-objective Learning to Rank (MOLTR). |
Chaosheng Dong; Michinari Momma; |
| 465 | Retrieval for Semantic People Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we present our experiments, learnings and solution to the problem of retrieval for people search. |
Rupesh Gupta; Chujie Zheng; Haojun Li; |
| 466 | Adaptive Domain Scaling for Personalized Sequential Modeling in Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Adaptive Domain Scaling (ADS) model, which comprehensively enhances the personalization capability in target-aware sequence modeling across multiple domains. |
Zheng Chai; Hui Lu; Di Chen; Qin Ren; Yuchao Zheng; Xun Zhou; |
| 467 | Negative Exclusion Filtering: Optimizing Ad Delivery Efficiency for Large-Scale Social Media Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These constraints can hinder model iteration and lead to incomplete ranking, causing regressions in user experience and ad performance.To address these issues, we analyzed ad ranking metrics and found that ad rankings for individual users remain relatively stable over short periods. Based on this insight, we introduce Negative Exclusion Filtering, a framework that optimizes the balance between ranked ad volume and computing resources. |
Min Zhang; Ganlin Song; Fang Zhou; Dong Liang; Jianwei Xiao; Shengyu Huang; Lizhang Qin; Xi Yan; Rong Shi; Xiyuan Chen; Jing Xu; Zhaojun Zhang; Ye Wang; Gautam Srinivasan; Yang Wang; Qianru Li; Mahesh Masale; Zhengyu Zhang; Zeliang Chen; Ellie Wen; Puneet Sharma; |
| 468 | Examples As The Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, crafting fully unbiased natural language prompts remains a challenge for humans. To address these challenges, we propose a novel framework, Examples as the Prompt (EaP). |
Jingying Zeng; Zhenwei Dai; Hui Liu; Samarth Varshney; Zhiji Liu; Chen Luo; Zhen Li; Qi He; Xianfeng Tang; |
| 469 | Post-event Modeling Via Causal Optimal Transport for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, post-event features, unavailable during inference, often face training-inference inconsistency and low coverage issues, especially post-click features like dwell time that are available only for clicked items. To address these challenges, we propose Causal Optimal Transport (COT), a novel framework that (1) generates pseudo post-click features via semi-supervised pseudo-labeling (2) causally generates accurate feature distributions using a Causal Distribution Shaper (CDS), and (3) refines generated features through optimal transport to minimize distributional divergence, facilitating further knowledge transfer. |
Yizhou Sang; Congcong Liu; Yuying Chen; Zhiwei Fang; Xue Jiang; Changping Peng; Zhangang Lin; Ching Law; Jingping Shao; |
| 470 | IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, optimizing models to handle such changes in real-time remains a major challenge in large-scale industrial settings. To address this, we propose the Interest-aware Representation and Alignment (IRA) framework, an efficient and scalable approach that dynamically adapts to new interactions through a cumulative structure. |
Youngjune Lee; Haeyu Jeong; Changgeon Lim; Jeong Choi; Hongjun Lim; Hangon Kim; Jiyoon Kwon; Saehun Kim; |
| 471 | ADORE: Autonomous Domain-Oriented Relevance Engine for E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose ADORE, a self-sustaining framework that synergizes three innovations: (1) A Rule-aware Relevance Discrimination module, where a Chain-of-Thought LLM generates intent-aligned training data, refined via Kahneman-Tversky Optimization (KTO) to align with user behavior; (2) An Error-type-aware Data Synthesis module that auto-generates adversarial examples to harden robustness; and (3) A Key-attribute-enhanced Knowledge Distillation module that injects domain-specific attribute hierarchies into a deployable student model. |
Zheng Fang; Donghao Xie; Ming Pang; Chunyuan Yuan; Xue Jiang; Changping Peng; Zhangang Lin; Zheng Luo; |
| 472 | Embedding-based Retrieval in Multi-Modal Content Moderation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While classification remains the dominant approach for content moderation, it often struggles in scenarios requiring rapid and cost-efficient responses, such as trend adaptation and urgent escalations. To address this issue, we introduce an Embedding-Based Retrieval (EBR) method designed to complement traditional classification approaches. |
Hanzhong Liang; Jinghao Shi; Xiang Shen; Zixuan Wang; Vera Wen; Ardalan Mehrani; Zhiqian Chen; Yifan Wu; Zhixin Zhang; |
| 473 | PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present PaRT, a novel framework enabling context-aware proactive dialogues for social chatbots through personalized real-time retrieval and generation. |
Zihan Niu; Zheyong Xie; Shaosheng Cao; Chonggang Lu; Zheyu Ye; Tong Xu; Zuozhu Liu; Yan Gao; Jia Chen; Zhe Xu; Yi Wu; Yao Hu; |
| 474 | GRAIN: Group-Reinforced Adaptive Interaction Network for Cold-Start CTR Prediction in E-commerce Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Notably, CSEs encompass novel users/items and new session search queries, each characterized by their limited interaction data and poor-quality embeddings, which collectively contribute to the complexity of CTR estimation.Existing studies predominantly address cold-start challenges in isolation, such as focusing separately on new users or new items, and lack a comprehensive framework to effectively integrate atomic ID features with group-level representations. To address these limitations, we propose GRAIN (Group Reinforced Adaptive Interaction Network), a novel framework that enhances CTR prediction across all maturity phases, namely Cold-Start, Warm-Up, and Common. |
Wei Bao; Hao Chen; Bang Lin; Tao Zhang; Chengfu Huo; |
| 475 | From Keywords to Concepts: A Late Interaction Approach to Semantic Product Search on IKEA.com Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional keyword-based retrieval struggles with complex, multi-attribute queries, potentially leading to suboptimal results and poor customer experience. To address these challenges, we introduce a late interaction-based semantic search engine designed for IKEA product search. |
Amritpal Singh Gill; Sannikumar Patel; P\'{e}ter Varga; Patrick Miller; Sakis Athanasiadis; |
| 476 | SuperRS: Multi Scenario Reciprocal-Aware Dual MoE for Unified Recommendation-Search Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While existing systems maintain separate pipelines for search and recommendation, these two scenarios share aligned objectives and exhibit consistent data patterns during ranking. To address this, we propose a joint modeling approach for search-recommendation ranking that enables information gain exchange between the two scenarios, thus facilitating enhanced modeling of users’ cross-scenario behaviors. |
Zihan Xia; Chuanyu Xu; Tao Zhang; Chengfu Huo; |
| 477 | MAAQR: An LLM-based Multi-Agent Framework for Adaptive Query Rewriting in Alipay Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite recent advancements, current rewriting approaches are still limited by an inadequate comprehension of domain-specific knowledge and a lack of mechanisms for adaptive refinement in response to new or changing query-item relationships. To overcome these limitations, we propose a large language model (LLM) based Multi-Agent Framework for Adaptive Query Rewriting (MAAQR) in Alipay Search. |
Qi Zheng; Mingjie Zhong; Saisai Gong; Huimin Jiang; Kaixin Wu; Hong Liu; Jia Xu; Linjian Mo; |
| 478 | Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We thereby propose an optimized GR framework designed to alleviate retrieval hallucination, which integrates knowledge distillation reasoning in model training and incorporate decision agent to further improve retrieval precision. |
Yedan Shen; Kaixin Wu; Yuechen Ding; Jingyuan Wen; Hong Liu; Mingjie Zhong; Zhouhan Lin; Jia Xu; Linjian Mo; |
| 479 | Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods, such as query log mining and vector matching, fail to optimize relevance, authenticity, and ad revenue of the rewrite.In this paper, we propose a novel Multi-objective aligned Bidword Generation Model (MoBGM), which includes a discriminator, generator, and preference alignment module. |
Zhenhui Liu; Chunyuan Yuan; Ming Pang; Zheng Fang; Li Yuan; Xue Jiang; Changping Peng; Zhangang Lin; Zheng Luo; Jingping Shao; |
| 480 | A System for Triggering Sports Instant Answers on Search Engines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we discuss various blocks of our scalable multilingual sports answer triggering pipeline. |
Ankith Karat; Atishay Tibrewal; Nishka Kotian; Manan Dang; Ravindra Valluri; Antony Ravi Teja Marineni; Sarthak Sahni; Rhea Sundaresan; Ankit Kumar; Aditya Mehndiratta; Sunil Shah; Arun D. Poondi; Chandra Bhushan; Subhasis Panigrahi; Manish Gupta; |
| 481 | Defining \& Optimizing Quality of LinkedIn’s Content Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we present our definition of relevance for content search, and the design of our content search system that enables optimization of Precision based on that definition. |
Ali Hooshmand; Rupesh Gupta; |
| 482 | Language Model Alignment for Conversational Shopping at Amazon Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we share our year-long journey of using language models for conversational shopping at Amazon and introduce how we use LLM fine-tuning techniques to enhance LLMs for a conversational shopping experience like Amazon Rufus. |
Chen Luo; Dimitri Papadimitriou; Hariharan Muralidharan; Dhineshkumar Ramasubbu; Aakash Kolekar; Wenju Xu; Cong Xu; Anirudh Srinivasan; Mukesh Jain; Qi He; |
| 483 | Inquiry Assistant Using LLM-Generated Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By transitioning to a chat-based approach, our method aims to handle ambiguous, incomplete, or nonspecific inquiries more effectively and enhance customer satisfaction with tailored, natural responses. |
Istv\'{a}n Varga; Yuta Yamashita; |
| 484 | Retrieval-Augmented Image Captioning and Generation with Entity Concepts Enhancement for Baidu Multimodal Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This inherent limitation subsequently leads to notable deficiencies in brand tonality, industry-specific relevance, and market adaptability of the generated advertising content. To address this challenge, we propose a multimodal ad content generation framework specifically engineered for online advertising system, particularly focused on resolving the deficiency in entity concepts. |
Lei Shen; Kang Zhao; Zhipeng Jin; Wen Tao; Yi Yang; Cong Han; Shuanglong Li; Zhongmin Cai; Lin Liu; |
| 485 | Optimize Visual Shopping Journey with Embedding-based Retrieval in Pinterest Closeup Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast to conventional EBR systems, we introduce complementary Shopping Priority Corpora that are prepared by probability models from a dynamic inventory with billions of candidates and highly skewed distributions. |
Junpeng Hou; Wei-Ting Lin; Arkin Dharawat; Jiaxing Qu; Qi Wang; Sai Xiao; Xianxing Zhang; Weiran Li; |
| 486 | Insight Agents: An LLM-Based Multi-Agent System for Data Insights Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our hypothesis is that IA will serve as a force multiplier for sellers, thereby driving incremental seller adoption by reducing the effort required and increase speed at which sellers make good business decisions. In this paper, we introduce this new LLM-backed end-to-end agentic workflow designed for comprehensive coverage, high accuracy, and low latency. |
Jincheng Bai; Zhenyu Zhang; Jennifer Zhang; Jason Zhu; |
| 487 | Towards Improving Image Quality in Second-Hand Marketplaces with LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the successful application of LLM capabilities as text-based task evaluators, we propose an approach that leverages multi-modal large language models (MLLMs) as evaluators of image quality. |
Sandra Garcia-Esparza; Victor Codina; Salma Lahbiss; Im\`{e}ne Sediri; Nafi Ciss\'{e}; |
| 488 | Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work discusses our efforts in developing initial UUM versions, practical challenges, technical choices and modeling and research directions with promising offline performance. |
Clark Mingxuan Ju; Leonardo Neves; Bhuvesh Kumar; Liam Collins; Tong Zhao; Yuwei Qiu; Qing Dou; Yang Zhou; Sohail Nizam; Rengim Aykan Ozturk; Yvette Liu; Sen Yang; Manish Malik; Neil Shah; |
| 489 | Content Moderation in TV Search: Balancing Policy Compliance, Relevance, and User Experience Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce an additional monitoring layer using Large Language Models (LLMs) to enhance content moderation. |
Adeep Hande; Kishorekumar Sundararajan; Sardar Hamidian; Ferhan Ture; |
| 490 | Towards More Relevant Product Search Ranking with Fulfillment Intent Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a model that predicts customer fulfillment intent based on the search query, past user interactions, and other contextual information. |
Jingxu Xu; Xinyi Liu; Yang Yu; Semih Yagli; Jingbo Liu; Cun Mu; |
| 491 | Ontology-Guided Knowledge Graph Retrieval for Multi-Hop and Cross-Granularity Store Fulfillment Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: requires precise, multi-hop reasoning across datasets with varying granularities. This paper introduces an ontology-based knowledge graph (KG) approach integrated with a structured text-to-Cypher generation pipeline, enabling accurate retrieval for such queries. |
Mengyue Zhao; Matthew Nokleby; Bo Shen; Wenbo Dong; Deepti Pachauri; Andrew Yang; |
| 492 | Large Scale Deployment of BERT Based Cross Encoder Model for Re-Ranking in Walmart Search Engine Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This challenge becomes more pronounced in the long-tail segment, where conventional techniques like caching prove ineffective. To tackle these issues, our paper introduces a scalable framework featuring a BERT-based cross encoder model for re-ranking, deployed in the Walmart search engine. |
Ajit Puthenputhussery; Changsung Kang; Alessandro Magnani; Tian Zhang; Hongwei Shang; Nitin Yadav; Prijith Chandran; Bhavin Madhani; Yuan-Tai Fu; He Wang; Zbigniew Gasiorek; Salvatore Tornatore; Srikanth Dasaka; Vivek Agrawal; Michael Bowersox; Cun Mu; Ciya Liao; |
| 493 | Progressive Refinement of E-commerce Search Ranking Based on Short-Term Activities of The Buyer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study presents a systematic approach to adapting e-commerce search results based on the current context. |
Taoran Sheng; Sathappan Muthiah; Atiq Islam; Jinming Feng; |
| 494 | Robust Inverse Retrieval in Online Advertising with Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a general framework for learning the inverse product retrieval process, a more complex procedure compared to the conventional forward approach. |
Phaniram Sayapaneni; Konstantin Shmakov; Sunil Goda; |
| 495 | Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a novel sequential recommendation model, Pyramid Mixer, which leverages the MLP-Mixer architecture to achieve efficient and complete modeling of user interests. |
Zhen Gong; Zhifang Fan; Hui Lu; Qiwei Chen; Chenbin Zhang; Lin Guan; Yuchao Zheng; Feng Zhang; Xiao Yang; Zuotao Liu; |
| 496 | Data-efficient Meta-models for Evaluation of Context-based Questions and Answers in LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a methodology combining efficient classification algorithms with dimensionality reduction techniques to minimize sample size demands while maintaining competitive performance. |
Julia Belikova; Konstantin Polev; Rauf Parchiev; Dmitry Simakov; |