Paper Digest: SIGIR 2023 Highlights
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TABLE 1: Paper Digest: SIGIR 2023 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study whether we can fundamentally neutralize the popularity bias in the aggregation process of GNN-based CF models. |
Huachi Zhou; Hao Chen; Junnan Dong; Daochen Zha; Chuang Zhou; Xiao Huang; |
| 2 | On The Impact of Outlier Bias on User Clicks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we identify and introduce the bias brought about by outlier items: users tend to click more on outlier items and their close neighbors. |
Fatemeh Sarvi; Ali Vardasbi; Mohammad Aliannejadi; Sebastian Schelter; Maarten de Rijke; |
| 3 | Rectifying Unfairness in Recommendation Feedback Loop Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, this creates a feedback loop in which the user is not longer recommended based on their true relevance score but instead based on biased training data. To address this problem of feedback loops, we propose a two-stage representation learning framework, B-FAIR, aimed at rectifying the unfairness caused by biased historical data in recommendation systems. |
Mengyue Yang; Jun Wang; Jean-Francois Ton; |
| 4 | Contrastive Box Embedding for Collaborative Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, as a cognitive task, it is essential for an impressive recommender system to acquire the cognitive capacity rather than to decide the users’ next steps by learning the pattern from the historical interactions through matching-based objectives. Therefore, in this paper, we propose to model the recommendation as a logical reasoning task which is more in line with an intelligent recommender system. |
Tingting Liang; Yuanqing Zhang; Qianhui Di; Congying Xia; Youhuizi Li; Yuyu Yin; |
| 5 | Learning to Re-rank with Constrained Meta-Optimal Transport Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel, fast, lightweight way to predict fair stochastic re-ranking policies: Constrained Meta-Optimal Transport (CoMOT). |
Andrés Hoyos-Idrobo; |
| 6 | Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present Ensemble Modeling with contrastive Knowledge Distillation for sequential recommendation (EMKD). |
Hanwen Du; Huanhuan Yuan; Pengpeng Zhao; Fuzhen Zhuang; Guanfeng Liu; Lei Zhao; Yanchi Liu; Victor S. Sheng; |
| 7 | MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel framework for SRS, called Mutual Enhancement of Long-Tailed user and item (MELT), that jointly alleviates the long-tailed problem in the perspectives of both users and items. |
Kibum Kim; Dongmin Hyun; Sukwon Yun; Chanyoung Park; |
| 8 | Frequency Enhanced Hybrid Attention Network for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we shift the perspective to the frequency domain, and propose a novel Frequency Enhanced Hybrid Attention Network for Sequential Recommendation, namely FEARec. |
Xinyu Du; Huanhuan Yuan; Pengpeng Zhao; Jianfeng Qu; Fuzhen Zhuang; Guanfeng Liu; Yanchi Liu; Victor S. Sheng; |
| 9 | Meta-optimized Contrastive Learning for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, not all contrastive pairs are always informative and discriminative enough for the training processing. Therefore, a more general CL-based recommendation model called Meta-optimized Contrastive Learning for sequential Recommendation (MCLRec) is proposed in this work. |
Xiuyuan Qin; Huanhuan Yuan; Pengpeng Zhao; Junhua Fang; Fuzhen Zhuang; Guanfeng Liu; Yanchi Liu; Victor Sheng; |
| 10 | Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we proposed a novel hierarchical preference modeling framework to substantially model the complex low- and high-level preference dynamics for accurate sequential recommendation. |
Chengkai Huang; Shoujin Wang; Xianzhi Wang; Lina Yao; |
| 11 | A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a new pipeline for KI-VQA tasks, consisting of a retriever and a reader. |
Alireza Salemi; Juan Altmayer Pizzorno; Hamed Zamani; |
| 12 | A Personalized Dense Retrieval Framework for Unified Information Access Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We develop a generic and extensible dense retrieval framework, called framework, that can handle a wide range of (personalized) information access requests, such as keyword search, query by example, and complementary item recommendation. |
Hansi Zeng; Surya Kallumadi; Zaid Alibadi; Rodrigo Nogueira; Hamed Zamani; |
| 13 | Constructing Tree-based Index for Efficient and Effective Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance. To solve this issue, we propose JTR, which stands for Joint optimization of TRee-based index and query encoding. |
Haitao Li; Qingyao Ai; Jingtao Zhan; Jiaxin Mao; Yiqun Liu; Zheng Liu; Zhao Cao; |
| 14 | One Blade for One Purpose: Advancing Math Information Retrieval Using Hybrid Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the most effective retriever for math remains impractical as it depends on token-level dense representations for each math token, which leads to prohibitive storage demands, especially considering that math content generally consumes more tokens. In this work, we try to alleviate this efficiency bottleneck while boosting math information retrieval effectiveness via hybrid search. |
Wei Zhong; Sheng-Chieh Lin; Jheng-Hong Yang; Jimmy Lin; |
| 15 | Lexically-Accelerated Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce ‘LADR’ (Lexically-Accelerated Dense Retrieval), a simple-yet-effective approach that improves the efficiency of existing dense retrieval models without compromising on retrieval effectiveness. |
Hrishikesh Kulkarni; Sean MacAvaney; Nazli Goharian; Ophir Frieder; |
| 16 | Multivariate Representation Learning for Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new representation learning framework for dense retrieval. |
Hamed Zamani; Michael Bendersky; |
| 17 | Large Language Models Are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, most existing methods struggle to reason over complex questions since the essential information is scattered in different places. To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning, and (ii) decompose a complex question into simpler sub-questions for text reasoning. |
Yunhu Ye; Binyuan Hui; Min Yang; Binhua Li; Fei Huang; Yongbin Li; |
| 18 | MGeo: Multi-Modal Geographic Language Model Pre-Training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel method for query-POI matching, namely Multi-modal Geographic language model (MGeo), which comprises a geographic encoder and a multi-modal interaction module. |
Ruixue Ding; Boli Chen; Pengjun Xie; Fei Huang; Xin Li; Qiang Zhang; Yao Xu; |
| 19 | Adapting Generative Pretrained Language Model for Open-domain Multimodal Sentence Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although existing methods have achieved compelling success, they still suffer from two key limitations: 1) lacking the adaptation of generative pre-trained language models for open-domain MMSS, and 2) lacking the explicit critical information modeling. To address these limitations, we propose a BART-MMSS framework, where BART is adopted as the backbone. |
Dengtian Lin; Liqiang Jing; Xuemeng Song; Meng Liu; Teng Sun; Liqiang Nie; |
| 20 | SciMine: An Efficient Systematic Prioritization Model Based on Richer Semantic Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These methods, even though achieving better performance than more sophisticated feature-based models such as BERT, omit rich and essential semantic information, therefore suffered from feature bias. In this study, we propose a novel framework SciMine to accelerate this screening process by capturing semantic feature representations from both background and the corpus. |
Fang Guo; Yun Luo; Linyi Yang; Yue Zhang; |
| 21 | Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Current strategies for using language models typically represent a concept by averaging the contextualised representations of its mentions in some corpus. This is potentially sub-optimal for at least two reasons. First, contextualised word vectors have an unusual geometry, which hampers downstream tasks. Second, concept embeddings should capture the semantic properties of concepts, whereas contextualised word vectors are also affected by other factors. To address these issues, we propose two contrastive learning strategies, based on the view that whenever two sentences reveal similar properties, the corresponding contextualised vectors should also be similar. |
Na Li; Hanane Kteich; Zied Bouraoui; Steven Schockaert; |
| 22 | Prompt Learning for News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recently, the pre-train, prompt, and predict paradigm, called prompt learning, has achieved many successes in natural language processing domain. In this paper, we make the first trial of this new paradigm to develop a Prompt Learning for News Recommendation (Prompt4NR) framework, which transforms the task of predicting whether a user would click a candidate news as a cloze-style mask-prediction task. |
Zizhuo Zhang; Bang Wang; |
| 23 | Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we aim to alleviate the Matthew effect in offline RL-based recommendation. |
Chongming Gao; Kexin Huang; Jiawei Chen; Yuan Zhang; Biao Li; Peng Jiang; Shiqi Wang; Zhong Zhang; Xiangnan He; |
| 24 | Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel risk-aware CLTR method with theoretical guarantees for safe deployment. |
Shashank Gupta; Harrie Oosterhuis; Maarten de Rijke; |
| 25 | HDNR: A Hyperbolic-Based Debiased Approach for Personalized News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, the existence of conformity bias, a potential cause of power-law distribution, may introduce biased guidance to learn user representations. In this paper, we propose a novel debiased method based on hyperbolic space, named HDNR, to tackle the above problems. |
Shicheng Wang; Shu Guo; Lihong Wang; Tingwen Liu; Hongbo Xu; |
| 26 | Measuring Item Global Residual Value for Fair Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items. |
Jiayin Wang; Weizhi Ma; Chumeng Jiang; Min Zhang; Yuan Zhang; Biao Li; Peng Jiang; |
| 27 | Distributionally Robust Sequential Recommnedation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While previous work has achieved remarkable successes, they mostly assume that the training and testing distributions are consistent, which may contradict with the diverse and complex user preferences, and limit the recommendation performance in real-world scenarios. To alleviate this problem, in this paper, we propose a robust sequential recommender framework to overcome the potential distribution shift between the training and testing sets. |
Rui Zhou; Xian Wu; Zhaopeng Qiu; Yefeng Zheng; Xu Chen; |
| 28 | LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, computing the attention matrix in traditional dot-product attention mechanisms results in a quadratic complexity with sequence lengths, leading to high computational costs for long-term sequential recommendation. Motivated by the above observation, we propose a novel L2-Normalized Linear Attention for the Transformer-based Sequential Recommender Systems (LinRec), which theoretically improves efficiency while preserving the learning capabilities of the traditional dot-product attention. |
Langming Liu; Liu Cai; Chi Zhang; Xiangyu Zhao; Jingtong Gao; Wanyu Wang; Yifu Lv; Wenqi Fan; Yiqi Wang; Ming He; Zitao Liu; Qing Li; |
| 29 | Poisoning Self-supervised Learning Based Sequential Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work shows that poisoning attacks against the pre-training stage threaten sequential recommender systems. |
Yanling Wang; Yuchen Liu; Qian Wang; Cong Wang; Chenliang Li; |
| 30 | Towards Multi-Interest Pre-training with Sparse Capsule Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Multi-Interest Pre-training with Sparse Capsule framework (named Miracle). |
Zuoli Tang; Lin Wang; Lixin Zou; Xiaolu Zhang; Jun Zhou; Chenliang Li; |
| 31 | Graph Masked Autoencoder for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, due to the hand-crafted property of their contrastive view generation strategies, existing CL-enhanced models i) can hardly yield consistent performance on diverse sequential recommendation tasks; ii) may not be immune to user behavior data noise. In light of this, we propose a simple yet effective Graph Masked AutoEncoder-enhanced sequential Recommender system (MAERec) that adaptively and dynamically distills global item transitional information for self-supervised augmentation. |
Yaowen Ye; Lianghao Xia; Chao Huang; |
| 32 | A Generic Learning Framework for Sequential Recommendation with Distribution Shifts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to develop a generic learning framework to enhance the generalization of recommenders in the dynamic environment. |
Zhengyi Yang; Xiangnan He; Jizhi Zhang; Jiancan Wu; Xin Xin; Jiawei Chen; Xiang Wang; |
| 33 | Single-shot Feature Selection for Multi-task Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, this paper proposes a novel Single-shot Feature Selection framework for MTRSs, referred to as MultiSFS, which is capable of selecting feature fields for each task while considering task relations in a single-shot manner. |
Yejing Wang; Zhaocheng Du; Xiangyu Zhao; Bo Chen; Huifeng Guo; Ruiming Tang; Zhenhua Dong; |
| 34 | Knowledge-enhanced Multi-View Graph Neural Networks for Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For local item-item relationships, conventional SBR only mines the sequence patterns while ignoring the feature patterns, which may introduce noise when learning users’ interests. To address these problems, we propose a novel Knowledge-enhanced Multi-View Graph Neural Network (KMVG) by constructing three views, namely knowledge view, session view, and pairwise view. |
Qian Chen; Zhiqiang Guo; Jianjun Li; Guohui Li; |
| 35 | Knowledge-refined Denoising Network for Robust Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of task-irrelevant knowledge propagation and vulnerability to interaction noise, which limits their performance. To solve these issues, we propose a robust knowledge-aware recommendation framework, called Knowledge-refined Denoising Network (KRDN), to prune the task-irrelevant knowledge associations and noisy implicit feedback simultaneously. |
Xinjun Zhu; Yuntao Du; Yuren Mao; Lu Chen; Yujia Hu; Yunjun Gao; |
| 36 | Mixed-Curvature Manifolds Interaction Learning for Knowledge Graph-aware Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The existing recommendation models built in a single type of embedding space do not have enough capacity to embrace various geometric patterns, consequently, resulting in suboptimal performance. To address this limitation, we propose a KG-aware recommendation model with mixed-curvature manifolds interaction learning, namely CurvRec. |
Jihu Wang; Yuliang Shi; Han Yu; Xinjun Wang; Zhongmin Yan; Fanyu Kong; |
| 37 | EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an enhanced encoder-decoder network (EEDN) to exploit rich latent features between users, POIs, and interactions between users and POIs for POI recommendation. |
Xinfeng Wang; Fumiyo Fukumoto; Jin Cui; Yoshimi Suzuki; Jiyi Li; Dongjin Yu; |
| 38 | Adaptive Graph Representation Learning for Next POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Adaptive Graph Representation-enhanced Attention Network (AGRAN) for next POI recommendation, which explores the utilization of graph structure learning to replace the pre-defined static graphs for learning more expressive representations of POIs. |
Zhaobo Wang; Yanmin Zhu; Chunyang Wang; Wenze Ma; Bo li; Jiadi Yu; |
| 39 | Spatio-Temporal Hypergraph Learning for Next POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Spatio-Temporal HyperGraph Convolutional Network (STHGCN). |
Xiaodong Yan; Tengwei Song; Yifeng Jiao; Jianshan He; Jiaotuan Wang; Ruopeng Li; Wei Chu; |
| 40 | Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. |
Xiao Zhang; Ziming Ye; Jianfeng Lu; Fuzhen Zhuang; Yanwei Zheng; Dongxiao Yu; |
| 41 | Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, users’ mobile devices in the real world have various hardware configurations (e.g., compute resources), leading to heterogeneous on-device models with different architectures and sizes. In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e.g., dimension & number of hidden layers). |
Jing Long; Tong Chen; Quoc Viet Hung Nguyen; Guandong Xu; Kai Zheng; Hongzhi Yin; |
| 42 | Learning Fine-grained User Interests for Micro-video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges of preference modeling and weak supervision signal, we propose a solution named FRAME (short for Fine-gRAined preference-modeling for Micro-video rEcommendation). |
Yu Shang; Chen Gao; Jiansheng Chen; Depeng Jin; Meng Wang; Yong Li; |
| 43 | Mining Stable Preferences: Adaptive Modality Decorrelation for Multimedia Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, when data (modal features) distribution shifts, the learned spurious preference might not guarantee to be as effective on inference as on the training set. Given that the statistical correlation between different modalities is a major cause of this problem, we propose a novel MOdality DEcorrelating STable learning framework, MODEST for brevity, to learn users’ stable preference. |
Jinghao Zhang; Qiang Liu; Shu Wu; Liang Wang; |
| 44 | DisCover: Disentangled Music Representation Learning for Cover Song Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we set the goal of disentangling version-specific and version-invariant factors, which could make it easier for the model to learn invariant music representations for unseen query songs. |
Jiahao Xun; Shengyu Zhang; Yanting Yang; Jieming Zhu; Liqun Deng; Zhou Zhao; Zhenhua Dong; Ruiqi Li; Lichao Zhang; Fei Wu; |
| 45 | A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a general framework to build scalable yet effective APC models for large-scale applications. |
Walid Bendada; Guillaume Salha-Galvan; Thomas Bouabça; Tristan Cazenave; |
| 46 | MEME: Multi-Encoder Multi-Expert Framework with Data Augmentation for Video Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a method called Graph Patch Spreading (GPS) to aggregate patches across frames at the coarse-grained level. |
Seong-Min Kang; Yoon-Sik Cho; |
| 47 | BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose BotMoE, a Twitter bot detection framework that jointly utilizes multiple user information modalities (metadata, textual content, network structure) to improve the detection of deceptive bots. |
Yuhan Liu; Zhaoxuan Tan; Heng Wang; Shangbin Feng; Qinghua Zheng; Minnan Luo; |
| 48 | Multi-behavior Self-supervised Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. |
Jingcao Xu; Chaokun Wang; Cheng Wu; Yang Song; Kai Zheng; Xiaowei Wang; Changping Wang; Guorui Zhou; Kun Gai; |
| 49 | Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource text classification in a two-pronged approach. |
Zhihao Wen; Yuan Fang; |
| 50 | Multi-Scenario Ranking with Adaptive Feature Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a multi-scenario ranking framework with adaptive feature learning (named MARIA). |
Yu Tian; Bofang Li; Si Chen; Xubin Li; Hongbo Deng; Jian Xu; Bo Zheng; Qian Wang; Chenliang Li; |
| 51 | LOAM: Improving Long-tail Session-based Recommendation Via Niche Walk Augmentation and Tail Session Mixup Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel method, LOAM, improving LOng-tail session-based recommendation via niche walk Augmentation and tail session Mixup, that alleviates popularity bias and enhances long-tail recommendation performance. |
Heeyoon Yang; YunSeok Choi; Gahyung Kim; Jee-Hyong Lee; |
| 52 | Curse of "Low" Dimensionality in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we showcase empirical evidence suggesting the necessity of sufficient dimensionality for user/item embeddings to achieve diverse, fair, and robust recommendation. |
Naoto Ohsaka; Riku Togashi; |
| 53 | Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches attempt to design novel late interactions instead of dot products, but they still fail to support complex feature interactions or lose retrieval efficiency. To address these challenges, we propose a new matching paradigm named SparCode, which supports not only sophisticated feature interactions but also efficient retrieval. |
Liangcai Su; Fan Yan; Jieming Zhu; Xi Xiao; Haoyi Duan; Zhou Zhao; Zhenhua Dong; Ruiming Tang; |
| 54 | An Offline Metric for The Debiasedness of Click Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an evaluation metric based on conditional independence testing to detect a lack of robustness to covariate shift in click models. |
Romain Deffayet; Philipp Hager; Jean-Michel Renders; Maarten de Rijke; |
| 55 | Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. |
Jiabang He; Yi Hu; Lei Wang; Xing Xu; Ning Liu; Hui Liu; Heng Tao Shen; |
| 56 | Smooth Operators for Effective Systematic Review Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the result sets of Boolean queries are unranked and difficult to control due to the strict Boolean operators. We address these problems in a single unified retrieval model by formulating a class of smooth operators that are compatible with and extend existing Boolean operators. |
Harrisen Scells; Ferdinand Schlatt; Martin Potthast; |
| 57 | Decoupled Hyperbolic Graph Attention Network for Cross-domain Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, the rich domain-shared syntactic and semantic information, which are respectively important for entity span detection and entity type classification, are still underutilized. In light of these two challenges, we propose applying graph attention networks (GATs) to encode the above two kinds of information. |
Jingyun Xu; Yi Cai; |
| 58 | Continual Learning on Dynamic Graphs Via Parameter Isolation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. |
Peiyan Zhang; Yuchen Yan; Chaozhuo Li; Senzhang Wang; Xing Xie; Guojie Song; Sunghun Kim; |
| 59 | Subgraph Search Over Neural-Symbolic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose neural-symbolic graph databases (NSGDs) that extends traditional graph data with content and structural embeddings in every node. |
Ye Yuan; Delong Ma; Anbiao Wu; Jianbin Qin; |
| 60 | StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a lightweight framework called StreamE towards the efficient generation of TKG representations in streaming scenarios. |
Jiasheng Zhang; Jie Shao; Bin Cui; |
| 61 | Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a user simulator-based framework for multi-turn interactions with a variety of mixed-initiative CS systems. |
Paul Owoicho; Ivan Sekulic; Mohammad Aliannejadi; Jeffrey Dalton; Fabio Crestani; |
| 62 | Explainable Conversational Question Answering Over Heterogeneous Sources Via Iterative Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our method EXPLAIGNN overcomes these limitations by integrating information from a mixture of sources with user-comprehensible explanations for answers. |
Philipp Christmann; Rishiraj Saha Roy; Gerhard Weikum; |
| 63 | Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel model, namely Multi-view Hypergraph Contrastive Policy Learning (MHCPL). |
Sen Zhao; Wei Wei; Xian-Ling Mao; Shuai Zhu; Minghui Yang; Zujie Wen; Dangyang Chen; Feida Zhu; |
| 64 | An Effective, Efficient, and Scalable Confidence-based Instance Selection Framework for Transformer-Based Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on Instance Selection (IS) – a set of methods focused on selecting the most representative documents for training, aimed at maintaining (or improving) classification effectiveness while reducing total time for training (or fine-tuning). |
Washington Cunha; Celso França; Guilherme Fonseca; Leonardo Rocha; Marcos André Gonçalves; |
| 65 | EDIndex: Enabling Fast Data Queries in Edge Storage Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. |
Qiang He; Siyu Tan; Feifei Chen; Xiaolong Xu; Lianyong Qi; Xinhong Hei; Hai Jin; Yun Yang; |
| 66 | Data-Aware Proxy Hashing for Cross-modal Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although these methods have made impressive progress, their generating processes of proxy hash codes are based only on the class information of the dataset or labels of data but do not take the data themselves into account. Therefore, these methods will probably generate some inappropriate proxy hash codes, thus damaging the retrieval performance of the hash models. To solve the aforementioned problem, we propose a novel Data-Aware Proxy Hashing for cross-modal retrieval, called DAPH. |
Rong-Cheng Tu; Xian-Ling Mao; Wenjin Ji; Wei Wei; Heyan Huang; |
| 67 | Asymmetric Hashing for Fast Ranking Via Neural Network Measures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and effective learning-to-hash approach for the fast item ranking problem that can be used to efficiently approximate any type of measure, including neural network measures. |
Khoa D. Doan; Shulong Tan; Weijie Zhao; Ping Li; |
| 68 | Continuous Input Embedding Size Search For Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose continuous input embedding size search (CIESS), a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from. |
Yunke Qu; Tong Chen; Xiangyu Zhao; Lizhen Cui; Kai Zheng; Hongzhi Yin; |
| 69 | Hear Me Out: A Study on The Use of The Voice Modality for Crowdsourced Relevance Assessments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given the rise of voice-based interfaces, we investigate whether it is feasible for assessors to judge the relevance of text documents via a voice-based interface. |
Nirmal Roy; Agathe Balayn; David Maxwell; Claudia Hauff; |
| 70 | Extending Label Aggregation Models with A Gaussian Process to Denoise Crowdsourcing Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We extend PGM-based LA models by integrating a GP prior on the true labels. |
Dan Li; Maarten de Rijke; |
| 71 | Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to its elusive and subjective nature, serendipity is difficult to study even with today’s advances in machine learning and deep learning techniques. Both ground truth data collecting and model developing are the open research questions. This paper addresses both the data and the model challenges for identifying serendipity in recommender systems. |
Zhe Fu; Xi Niu; Li Yu; |
| 72 | Dataset Preparation for Arbitrary Object Detection: An Automatic Approach Based on Web Information in English Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we leverage information from the web, and propose a fully-automatic dataset preparation mechanism without any human annotation, which can automatically prepare a high-quality training dataset for the detection task with English text terms describing target objects. |
Shucheng Li; Boyu Chang; Bo Yang; Hao Wu; Sheng Zhong; Fengyuan Xu; |
| 73 | InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a convolutional architecture InceptionXML which is light-weight, yet powerful, and robust to the inherent lack of word-order in short-text queries encountered in search and recommendation. |
Siddhant Kharbanda; Atmadeep Banerjee; Devaansh Gupta; Akash Palrecha; Rohit Babbar; |
| 74 | An Effective Framework for Enhancing Query Answering in A Heterogeneous Data Lake Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose LakeAns that semantically integrates heterogeneous data schemas of the lake to enhance the semantics of query answers. |
Qin Yuan; Ye Yuan; Zhenyu Wen; He Wang; Shiyuan Tang; |
| 75 | BeamQA: Multi-hop Knowledge Graph Question Answering with Sequence-to-Sequence Prediction and Beam Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing KGQA frameworks that use such techniques often depend on learning a transformation from the query representation to the graph embedding space, which requires access to a large training dataset. We present BeamQA, an approach that overcomes these limitations by combining a sequence-to-sequence prediction model with beam search execution in the embedding space. |
Farah Atif; Ola El Khatib; Djellel Difallah; |
| 76 | Leader-Generator Net: Dividing Skill and Implicitness for Conquering FairytaleQA Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, a simple but effective Leader-Generator Network is proposed to explicitly separate and extract fine-grained reading skills and the implicitness or explicitness of the question. |
Wei Peng; Wanshui Li; Yue Hu; |
| 77 | Unsupervised Story Discovery from Continuous News Streams Via Scalable Thematic Embedding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel thematic embedding with an off-the-shelf pretrained sentence encoder to dynamically represent articles and stories by considering their shared temporal themes. |
Susik Yoon; Dongha Lee; Yunyi Zhang; Jiawei Han; |
| 78 | BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate methods for incorporating temporal information during pre-training to further improve the performance on time-related tasks. |
Jiexin Wang; Adam Jatowt; Masatoshi Yoshikawa; Yi Cai; |
| 79 | Time-interval Aware Share Recommendation Via Bi-directional Continuous Time Dynamic Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Even worse, users may keep inactive during some periods, which results in difficulties in updating personalized profiles. To address the above challenges, in this paper, we propose a dynamic graph share recommendation model called DynShare. |
Ziwei Zhao; Xi Zhu; Tong Xu; Aakas Lizhiyu; Yu Yu; Xueying Li; Zikai Yin; Enhong Chen; |
| 80 | Diffusion Recommender Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In light of the impressive advantages of Diffusion Models (DMs) over traditional generative models in image synthesis, we propose a novel Diffusion Recommender Model (named DiffRec) to learn the generative process in a denoising manner. |
Wenjie Wang; Yiyan Xu; Fuli Feng; Xinyu Lin; Xiangnan He; Tat-Seng Chua; |
| 81 | Understand The Dynamic World: An End-to-End Knowledge Informed Framework for Open Domain Entity State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it’s challenging as the model needs to predict an arbitrary number of entity state changes caused by the action while most of the entities are implicitly relevant to the actions and their attributes as well as states are from open vocabularies. To tackle these challenges, we propose a novel end-to-end Knowledge Informed framework for open domain Entity State Tracking, namely KIEST, which explicitly retrieves the relevant entities and attributes from external knowledge graph (i.e., ConceptNet) and incorporates them to autoregressively generate all the entity state changes with a novel dynamic knowledge grained encoder-decoder framework. |
Mingchen Li; Lifu Huang; |
| 82 | Weighted Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose WeightE, which attends differentially to different entities and relations. |
Zhao Zhang; Zhanpeng Guan; Fuwei Zhang; Fuzhen Zhuang; Zhulin An; Fei Wang; Yongjun Xu; |
| 83 | Relation-Aware Multi-Positive Contrastive Knowledge Graph Completion with Embedding Dimension Scaling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, contrastive loss with single positive sample learns little structural and semantic information in knowledge graphs due to the complex relation types. To address these challenges, we propose a novel knowledge graph completion model named ConKGC with the embedding dimension scaling and a relation-aware multi-positive contrastive loss. |
Bin Shang; Yinliang Zhao; Di Wang; Jun Liu; |
| 84 | Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we extend the fully-inductive setting, where entities in the training and test sets are totally disjoint, into TKGs and take a further step towards a more flexible and time-sensitive temporal relation prediction approach SST-BERT,incorporating Structured Sentences with Time-enhanced BERT. |
Zhongwu Chen; Chengjin Xu; Fenglong Su; Zhen Huang; Yong Dou; |
| 85 | Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC). |
Linhao Luo; Yuan-Fang Li; Gholamreza Haffari; Shirui Pan; |
| 86 | Schema-aware Reference As Prompt Improves Data-Efficient Knowledge Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing prompt-based learning methods for knowledge graph construction are still susceptible to several potential limitations: (i) semantic gap between natural language and output structured knowledge with pre-defined schema, which means model cannot fully exploit semantic knowledge with the constrained templates; (ii) representation learning with locally individual instances limits the performance given the insufficient features, which are unable to unleash the potential analogical capability of pre-trained language models. Motivated by these observations, we propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP), for data-efficient knowledge graph construction. |
Yunzhi Yao; Shengyu Mao; Ningyu Zhang; Xiang Chen; Shumin Deng; Xi Chen; Huajun Chen; |
| 87 | Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose contrastive state augmentations (CSA) for the training of RL-based recommender systems. |
Zhaochun Ren; Na Huang; Yidan Wang; Pengjie Ren; Jun Ma; Jiahuan Lei; Xinlei Shi; Hengliang Luo; Joemon Jose; Xin Xin; |
| 88 | Improving Implicit Feedback-Based Recommendation Through Multi-Behavior Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing studies that attempted to learn from multiple types of user behavior often fail to: (i) learn universal and accurate user preferences from different behavioral data distributions, and (ii) overcome the noise and bias in observed implicit user feedback. To address the above problems, we propose multi-behavior alignment (MBA), a novel recommendation framework that learns from implicit feedback by using multiple types of behavioral data. |
Xin Xin; Xiangyuan Liu; Hanbing Wang; Pengjie Ren; Zhumin Chen; Jiahuan Lei; Xinlei Shi; Hengliang Luo; Joemon M. Jose; Maarten de Rijke; Zhaochun Ren; |
| 89 | When Newer Is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a set of evaluation strategies for measuring memorization performance, generalization performance, and subgroup-specific performance of recommendation models. |
Yushun Dong; Jundong Li; Tobias Schnabel; |
| 90 | Session Search with Pre-trained Graph Classification Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is non-trivial to model the intra-session interactions and complicated structural patterns among the previously issued queries, clicked documents, as well as the terms or entities that appeared in them. To solve this problem, in this paper, we propose a novel Session Search with Graph Classification Model (SSGC), which regards session search as a graph classification task on a heterogeneous graph that represents the search history in each session. |
Shengjie Ma; Chong Chen; Jiaxin Mao; Qi Tian; Xuhui Jiang; |
| 91 | Multi-order Matched Neighborhood Consistent Graph Alignment in A Union Vector Space Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the unsupervised plain graph alignment problem, which aims to find node correspondences across two graphs without any side information. |
Wei Tang; Haifeng Sun; Jingyu Wang; Qi Qi; Jing Wang; Hao Yang; Shimin Tao; |
| 92 | Personalized Federated Relation Classification Over Heterogeneous Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to investigate personalized federated relation classification, in which strong client models adapted to their own data are desired. |
Ning Pang; Xiang Zhao; Weixin Zeng; Ji Wang; Weidong Xiao; |
| 93 | Leveraging Transferable Knowledge Concept Graph Embedding for Cold-Start Cognitive Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the cold-start issue, we propose a two-stage solution named TechCD (Transferable knowledgE Concept grapH embedding framework for Cognitive Diagnosis). |
Weibo Gao; Hao Wang; Qi Liu; Fei Wang; Xin Lin; Linan Yue; Zheng Zhang; Rui Lv; Shijin Wang; |
| 94 | Editable User Profiles for Controllable Text Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this problem by proposing LACE, a novel concept value bottleneck model for controllable text recommendations. |
Sheshera Mysore; Mahmood Jasim; Andrew Mccallum; Hamed Zamani; |
| 95 | Intent-aware Ranking Ensemble for Personalized Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we treat a user’s possible behaviors and the potential interacting item categories as the user’s intent. |
Jiayu Li; Peijie Sun; Zhefan Wang; Weizhi Ma; Yangkun Li; Min Zhang; Zhoutian Feng; Daiyue Xue; |
| 96 | Personalized Retrieval Over Millions of Items Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite being scalable, this strategy risks losing items uniquely relevant to a user that fail to get shortlisted during non-personalized retrieval. This paper bridges this gap by developing the XPERT algorithm that identifies a form of two-sided personalization that can be scalably implemented over millions of items and hundreds of millions of users. |
Hemanth Vemuri; Sheshansh Agrawal; Shivam Mittal; Deepak Saini; Akshay Soni; Abhinav V. Sambasivan; Wenhao Lu; Yajun Wang; Mehul Parsana; Purushottam Kar; Manik Varma; |
| 97 | ML-LJP: Multi-Law Aware Legal Judgment Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, following the actual legal process, we expand the law article prediction as a multi-label classification task that includes both the charge-related law articles and term-related law articles and propose a novel multi-law aware LJP (ML-LJP) method to improve the performance of LJP. |
Yifei Liu; Yiquan Wu; Yating Zhang; Changlong Sun; Weiming Lu; Fei Wu; Kun Kuang; |
| 98 | SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing pre-trained language models designed for general purposes have not been equipped to handle legal elements. To address these issues, in this paper, we propose SAILER, a new Structure-Aware pre-traIned language model for LEgal case Retrieval. |
Haitao Li; Qingyao Ai; Jia Chen; Qian Dong; Yueyue Wu; Yiqun Liu; Chong Chen; Qi Tian; |
| 99 | Creating A Silver Standard for Patent Simplification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an approach to automatically simplify patent text through rephrasing. |
Silvia Casola; Alberto Lavelli; Horacio Saggion; |
| 100 | Not Just Skipping: Understanding The Effect of Sponsored Content on Users’ Decision-Making in Online Health Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To promote sales, advertisers are willing to pay search engines to promote their content to a prominent position in the search result page (SERP). This raises concerns about the search engine manipulation effect (SEME): the opinions of users can be influenced by the way search results are presented In this work, we investigate the connection between SEME and sponsored content in the health domain. |
Anat Hashavit; Hongning Wang; Tamar Stern; Sarit Kraus; |
| 101 | Cone: Unsupervised Contrastive Opinion Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods tend to generate aspect clusters with incoherent sentences, conflicting viewpoints, redundant aspects. To address these problems, we propose a novel unsupervised Contrastive OpinioN Extraction model, called Cone, which learns disentangled latent aspect and sentiment representations based on pseudo aspect and sentiment labels by combining contrastive learning with iterative aspect/sentiment clustering refinement. |
Runcong Zhao; Lin Gui; Yulan He; |
| 102 | AdaMCL: Adaptive Fusion Multi-View Contrastive Learning for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel adaptive multi-view fusion contrastive learning framework, named AdaMCL, for graph collaborative filtering. |
Guanghui Zhu; Wang Lu; Chunfeng Yuan; Yihua Huang; |
| 103 | Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose øurs, a triple structural information modeling method for accurate, explainable and interactive recommendation on dynamic interaction graphs. |
Jiahao Liu; Dongsheng Li; Hansu Gu; Tun Lu; Peng Zhang; Li Shang; Ning Gu; |
| 104 | Blurring-Sharpening Process Models for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Being inspired by recent successes of graph filtering-based methods and score-based generative models (SGMs), we present a novel concept of blurring-sharpening process model (BSPM). |
Jeongwhan Choi; Seoyoung Hong; Noseong Park; Sung-Bae Cho; |
| 105 | Collaborative Residual Metric Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Further analysis also uncovers a link between the normalization strength of interaction signals and the novelty of recommendation, which has been overlooked by existing studies. Based on the above findings, we propose a novel model to learn a generalized distance user-item distance metric to capture user preference in interaction signals by modeling the residuals of distance. |
Tianjun Wei; Jianghong Ma; Tommy W.S. Chow; |
| 106 | Generative-Contrastive Graph Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph. To tackle the above limitations, we propose a novel Variational Graph Generative-Contrastive Learning (VGCL) framework for recommendation. |
Yonghui Yang; Zhengwei Wu; Le Wu; Kun Zhang; Richang Hong; Zhiqiang Zhang; Jun Zhou; Meng Wang; |
| 107 | Hydrus: Improving Personalized Quality of Experience in Short-form Video Services Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In other words, some users would accept a 20ms increase in latency to enjoy higher-quality videos, while others prioritize minimizing lag above all else. Inspired by this, we present Hydrus, a novel resource allocation system that delivers the best possible personalized QoE by making tradeoffs between response latency and recommendation accuracy. |
Zhiyu Yuan; Kai Ren; Gang Wang; Xin Miao; |
| 108 | Disentangled Contrastive Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact that user-item interaction behaviors are often driven by diverse latent intent factors (e.g., shopping for family party, preferred color or brand of products); ii) Their introduced non-adaptive augmentation techniques are vulnerable to noisy information, which raises concerns about the model’s robustness and the risk of incorporating misleading self-supervised signals. In light of these limitations, we propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation in an adaptive fashion. |
Xubin Ren; Lianghao Xia; Jiashu Zhao; Dawei Yin; Chao Huang; |
| 109 | Aligning Distillation For Cold-start Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, generative models may over-recommend either warm or cold items, neglecting the other type, and dropout models may negatively impact warm item recommendations. To address this, we propose the Aligning Distillation (ALDI) framework, which leverages warm items as "teachers" to transfer their behavioral information to cold items, referred to as "students". |
Feiran Huang; Zefan Wang; Xiao Huang; Yufeng Qian; Zhetao Li; Hao Chen; |
| 110 | M2EU: Meta Learning for Cold-start Recommendation Via Enhancing User Preference Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we argue that the user representation learned in this way may be inadequate to capture user preference well since solely utilizing his/her own interactions may be far from enough in cold-start scenarios. To tackle this problem, we propose a novel meta-learning method named M2EU to enrich the representations of cold-start users by incorporating the information from other similar users who are identified based on the similarity of both inherent attributes and historical interactions. |
Zhenchao Wu; Xiao Zhou; |
| 111 | A Preference Learning Decoupling Framework for User Cold-Start Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response to the aforementioned issues, we propose a preference learning decoupling framework, which is enhanced with meta-augmentation (PDMA), for user cold-start recommendation. |
Chunyang Wang; Yanmin Zhu; Aixin Sun; Zhaobo Wang; Ke Wang; |
| 112 | Exploring Scenarios of Uncertainty About The Users’ Preferences in Interactive Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there are at least two scenarios of uncertainty about the users’ preferences over their journey: (1) when the user joins for the first time and (2) when the system continually makes wrong recommendations because of prior misleading assumptions. In this work, we introduce concepts from the Active Learning theory to mitigate the impact of such scenarios. |
Nícollas Silva; Thiago Silva; Henrique Hott; Yan Ribeiro; Adriano Pereira; Leonardo Rocha; |
| 113 | Topic-enhanced Graph Neural Networks for Extraction-based Explainable Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we focus on the explicit and implicit analysis of review information simultaneously and propose novel a Topic-enhanced Graph Neural Networks (TGNN) to fully explore review information for better explainable recommendations. |
Jie Shuai; Le Wu; Kun Zhang; Peijie Sun; Richang Hong; Meng Wang; |
| 114 | Strategy-aware Bundle Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the remarkable performance of existing approaches, we argue that they seldom consider the bundling strategy (i.e., how the items within a bundle are associated with each other) in the bundle recommendation, resulting in the suboptimal user and bundle representations for their interaction prediction. Therefore, we propose to model the strategy-aware user and bundle representations for the bundle recommendation. |
Yinwei Wei; Xiaohao Liu; Yunshan Ma; Xiang Wang; Liqiang Nie; Tat-Seng Chua; |
| 115 | Soft Prompt Decoding for Multilingual Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages. |
Zhiqi Huang; Hansi Zeng; Hamed Zamani; James Allan; |
| 116 | BLADE: Combining Vocabulary Pruning and Intermediate Pretraining for Scaleable Neural CLIR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, the representations of terms from different languages with similar meanings might not be sufficiently similar. To address these issues, we propose a learned sparse representation model, BLADE, combining vocabulary pruning with intermediate pre-training based on cross-language supervision. |
Suraj Nair; Eugene Yang; Dawn Lawrie; James Mayfield; Douglas W. Oard; |
| 117 | Representation and Labeling Gap Bridging for Cross-lingual Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most current work relies on general multilingual language models to represent text, and then uses classic combined tagging (e.g., B-ORG) to annotate entities; However, this approach neglects the lack of cross-lingual alignment of entity representations in language models, and also ignores the fact that entity spans and types have varying levels of labeling difficulty in terms of transferability. To address these challenges, we propose a novel framework, referred to as DLBri, which addresses the issues of representation and labeling simultaneously. |
Xinghua Zhang; Bowen Yu; Jiangxia Cao; Quangang Li; Xuebin Wang; Tingwen Liu; Hongbo Xu; |
| 118 | Rethinking Benchmarks for Cross-modal Image-text Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With the prevalence of large scale multimodal pretraining models, several state-of-the-art models (e.g. X-VLM) have achieved near-perfect performance on widely-used image-text retrieval benchmarks, i.e. MSCOCO-Test-5K and Flickr30K-Test-1K. In this paper, we review the two common benchmarks and observe that they are insufficient to assess the true capability of models on fine-grained cross-modal semantic matching. |
Weijing Chen; Linli Yao; Qin Jin; |
| 119 | Learnable Pillar-based Re-ranking for Image-Text Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we analyze the reason from four perspectives, i.e., generalization, flexibility, sparsity, and asymmetry, and propose a novel learnable pillar-based re-ranking paradigm. |
Leigang Qu; Meng Liu; Wenjie Wang; Zhedong Zheng; Liqiang Nie; Tat-Seng Chua; |
| 120 | Keyword-Based Diverse Image Retrieval By Semantics-aware Contrastive Learning and Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As a result, irrelevant images and images of rare or unique semantics may be projected inappropriately, which degrades the relevance and diversity of the results generated by some typical algorithms like top-k. To cope with these problems, this paper presents a new method called CoLT that tries to generate much more representative and robust representations for accurately classifying images. |
Minyi Zhao; Jinpeng Wang; Dongliang Liao; Yiru Wang; Huanzhong Duan; Shuigeng Zhou; |
| 121 | From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As the attribute-specific similarity typically corresponds to the specific subtle regions of images, we propose a Region-to-Patch Framework (RPF) that consists of a region-aware branch and a patch-aware branch to extract fine-grained attribute-related visual features for precise retrieval in a coarse-to-fine manner. |
Jianfeng Dong; Xiaoman Peng; Zhe Ma; Daizong Liu; Xiaoye Qu; Xun Yang; Jixiang Zhu; Baolong Liu; |
| 122 | Multi-view Multi-aspect Neural Networks for Next-basket Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing NBR methods suffer from two limitations: (1) overlooking low-level item correlations, which results in coarse-grained item representation; and (2) failing to consider spurious interests in repeated behaviors, leading to suboptimal user interest learning. To address these limitations, we propose a novel solution named Multi-view Multi-aspect Neural Recommendation (MMNR) for NBR, which first normalizes the interactions from both the user-side and item-side, respectively, aiming to remove the spurious interests, and utilizes them as weights for items from different views to construct differentiated representations for each interaction item, enabling comprehensive user interest learning. |
Zhiying Deng; Jianjun Li; Zhiqiang Guo; Wei Liu; Li Zou; Guohui Li; |
| 123 | Cross-Market Product-Related Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We conduct a data analysis to understand the scope of the cross-market question-answering task. |
Negin Ghasemi; Mohammad Aliannejadi; Hamed Bonab; Evangelos Kanoulas; Arjen P. de Vries; James Allan; Djoerd Hiemstra; |
| 124 | Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To that end, in this paper, we construct a hypergraph from basket-wise purchasing records and probe the inter-basket and intra-basket item relations behind the hyperedges. |
Ran Li; Liang Zhang; Guannan Liu; Junjie Wu; |
| 125 | When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users’ search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors. |
Zihua Si; Zhongxiang Sun; Xiao Zhang; Jun Xu; Xiaoxue Zang; Yang Song; Kun Gai; Ji-Rong Wen; |
| 126 | Unsupervised Readability Assessment Via Learning from Weak Readability Signals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework to Learn a neural model from Weak Readability Signals (LWRS). |
Yuliang Liu; Zhiwei Jiang; Yafeng Yin; Cong Wang; Sheng Chen; Zhaoling Chen; Qing Gu; |
| 127 | What If: Generating Code to Answer Simulation Questions in Chemistry Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a neural program synthesis approach based on reinforcement learning with a novel state-transition semantic reward. |
Gal Peretz; Mousa Arraf; Kira Radinsky; |
| 128 | ErrorCLR: Semantic Error Classification, Localization and Repair for Introductory Programming Assignments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, existing approaches often leverage rule-based methods and evaluate them with a small number of programming assignments. To tackle the problems, we first describe the creation of a new dataset COJ2022 that contains 5,914 C programs with semantic errors submitted to 498 different assignments in an introductory programming course, where each program is annotated with the error types and locations and is coupled with the repaired program submitted by the same student. We show the advantages of COJ2022 over existing datasets on various aspects. Second, we treat semantic error classification, localization and repair as dependent tasks, and propose a novel two-stage method ErrorCLR to solve them. |
Siqi Han; Yu Wang; Xuesong Lu; |
| 129 | A Geometric Framework for Query Performance Prediction in Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: QPP for the CS domain is a relatively new field and lacks proper framing. In this study, we address this gap by proposing a framework for the application of QPP in the CS domain and use it to evaluate the performance of predictors. |
Guglielmo Faggioli; Nicola Ferro; Cristina Ioana Muntean; Raffaele Perego; Nicola Tonellotto; |
| 130 | DMBIN: A Dual Multi-behavior Interest Network for Click-Through Rate Prediction Via Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, previous methods have yet to study this phenomenon well, which limits the recommendation performance. To tackle this challenge, we propose a novel Dual Multi-Behavior Interest Network (DMBIN for short) to disentangle behavior-specific and behavioral-invariant interests from various behaviors for a better recommendation. |
Tianqi He; Kaiyuan Li; Shan Chen; Haitao Wang; Qiang Liu; Xingxing Wang; Dong Wang; |
| 131 | EulerNet: Adaptive Feature Interaction Learning Via Euler’s Formula for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler’s formula. |
Zhen Tian; Ting Bai; Wayne Xin Zhao; Ji-Rong Wen; Zhao Cao; |
| 132 | Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we reformulate the CTR task — instead of pursuing ERM on historical data, we split the historical data chronologically into several periods (a.k.a, environments), aiming to learn feature interactions that are stable across periods. |
Yang Zhang; Tianhao Shi; Fuli Feng; Wenjie Wang; Dingxian Wang; Xiangnan He; Yongdong Zhang; |
| 133 | News Popularity Beyond The Click-Through-Rate for Personalized Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our aim is to create awareness about the different perspectives of measuring popularity while discussing the advantages and disadvantages of the proposed metrics with respect to the human click behavior. |
Ashutosh Nayak; Mayur Garg; Rajasekhara Reddy Duvvuru Muni; |
| 134 | Online Conversion Rate Prediction Via Neural Satellite Networks in Delayed Feedback Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Delayed Feed-back modeling via neural Satellite Networks (DFSN for short) for online CVR prediction. |
Qiming Liu; Haoming Li; Xiang Ao; Yuyao Guo; Zhihong Dong; Ruobing Zhang; Qiong Chen; Jianfeng Tong; Qing He; |
| 135 | A Topic-aware Summarization Framework with Different Modal Side Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a general summarization framework, which can flexibly incorporate various modalities of side information. |
Xiuying Chen; Mingzhe Li; Shen Gao; Xin Cheng; Qiang Yang; Qishen Zhang; Xin Gao; Xiangliang Zhang; |
| 136 | Can ChatGPT Write A Good Boolean Query for Systematic Review Literature Search? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate ChatGPT as a means for automatically formulating and refining complex Boolean queries for systematic review literature search. |
Shuai Wang; Harrisen Scells; Bevan Koopman; Guido Zuccon; |
| 137 | FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce FiD-Light to strongly increase the efficiency of the state-of-the-art retrieval-augmented FiD model, while maintaining the same level of effectiveness. |
Sebastian Hofstätter; Jiecao Chen; Karthik Raman; Hamed Zamani; |
| 138 | A Unified Generative Retriever for Knowledge-Intensive Language Tasks Via Prompt Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a unified generative retriever (UGR) that combines task-specific effectiveness with robust performance over different retrieval tasks in KILTs. |
Jiangui Chen; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Yiqun Liu; Yixing Fan; Xueqi Cheng; |
| 139 | RHB-Net: A Relation-aware Historical Bridging Network for Text2SQL Auto-Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, despite recent improvement, existing Text2SQL proposals allow only input in the form of complete questions. This leaves behind users who struggle to formulate complete questions, e.g., because they lack database expertise or are unfamiliar with the underlying database schema. To address this shortcoming, we study the novel problem of Text2SQL Auto-Completion (TSAC) that extends Text2SQL to also take partial or incomplete questions as input. |
Bolong Zheng; Lei Bi; Ruijie Xi; Lu Chen; Yunjun Gao; Xiaofang Zhou; Christian S. Jensen; |
| 140 | M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Therefore, how to distill useful interests is crucial. To tackle the above two problems, we propose a metapath and multi-interest aggregated graph neural network (M2GNN). |
Zepeng Huai; Yuji Yang; Mengdi Zhang; Zhongyi Zhang; Yichun Li; Wei Wu; |
| 141 | AutoTransfer: Instance Transfer for Cross-Domain Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, the extracted implicit information only appears within the intermediate substructure of specific CDRs during training and is thus not easily retained for more use. In light of these challenges, this paper proposes AutoTransfer, with an Instance Transfer Policy Network, to selectively transfers instances from source domain to target domain for improved recommendations. |
Jingtong Gao; Xiangyu Zhao; Bo Chen; Fan Yan; Huifeng Guo; Ruiming Tang; |
| 142 | Beyond The Overlapping Users: Cross-Domain Recommendation Via Adaptive Anchor Link Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we view the anchor links between users of various domains as the learnable parameters to learn the task-relevant cross-domain correlations. |
Yi Zhao; Chaozhuo Li; Jiquan Peng; Xiaohan Fang; Feiran Huang; Senzhang Wang; Xing Xie; Jibing Gong; |
| 143 | PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel prompt-enhanced paradigm for multi-scenario recommendation. |
Yuhao Wang; Xiangyu Zhao; Bo Chen; Qidong Liu; Huifeng Guo; Huanshuo Liu; Yichao Wang; Rui Zhang; Ruiming Tang; |
| 144 | LightGT: A Light Graph Transformer for Multimedia Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To capture the informative features from the extracted ones, we resort to Transformer model to establish the correlation between the items historically interacted by the same user. Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT). |
Yinwei Wei; Wenqi Liu; Fan Liu; Xiang Wang; Liqiang Nie; Tat-Seng Chua; |
| 145 | Dual Semantic Knowledge Composed Multimodal Dialog Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although existing studies have achieved fruitful progress, they still suffer from two critical limitations: 1) focusing on the attribute knowledge but ignoring the relation knowledge that can reveal the correlations between different entities and hence promote the response generation, and 2)only conducting the cross-entropy loss based output-level supervision but lacking the representation-level regularization. To address these limitations, we devise a novel multimodal task-oriented dialog system (named MDS-S2). |
Xiaolin Chen; Xuemeng Song; Yinwei Wei; Liqiang Nie; Tat-Seng Chua; |
| 146 | MAMO: Fine-Grained Vision-Language Representations Learning with Masked Multimodal Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a jointly masked multimodal modeling method to learn fine-grained multimodal representations. |
Zijia Zhao; Longteng Guo; Xingjian He; Shuai Shao; Zehuan Yuan; Jing Liu; |
| 147 | Multimodal Counterfactual Learning Network for Multimedia-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we argue that the multimodal content of user-uninteracted items can be further exploited to identify and eliminate the user preference-irrelevant portion inside user-interacted multimodal content, for example by counterfactual inference of causal theory. |
Shuaiyang Li; Dan Guo; Kang Liu; Richang Hong; Feng Xue; |
| 148 | Law Article-Enhanced Legal Case Matching: A Causal Learning Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In light of the observation, this paper proposes a model-agnostic causal learning framework called Law-Match, under which the legal case matching models are learned by respecting the corresponding law articles. |
Zhongxiang Sun; Jun Xu; Xiao Zhang; Zhenhua Dong; Ji-Rong Wen; |
| 149 | Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, such insufficient mining manners hinder expressive ability, leading to sub-optimal performances. To address these limitations, we propose a novel reasoning model, termed RPC, which sufficiently mines the information underlying the Relational correlations and Periodic patterns via two novel Correspondence units, i.e., relational correspondence unit (RCU) and periodic correspondence unit (PCU). |
Ke Liang; Lingyuan Meng; Meng Liu; Yue Liu; Wenxuan Tu; Siwei Wang; Sihang Zhou; Xinwang Liu; |
| 150 | Dynamic Mixed Membership Stochastic Block Model for Weighted Labeled Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, a new family of Mixed Membership Stochastic Block Models (MMSBM) allows to model static labeled networks under the assumption of mixed-membership clustering. In this work, we propose to extend this later class of models to infer dynamic labeled networks under a mixed membership assumption. |
Gaël Poux-Médard; Julien Velcin; Sabine Loudcher; |
| 151 | DREAM: Adaptive Reinforcement Learning Based on Attention Mechanism for Temporal Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the performance of RL-based TKG reasoning methods is limited due to: (1) lack of ability to capture temporal evolution and semantic dependence jointly; (2) excessive reliance on manually designed rewards. To overcome these challenges, we propose an adaptive reinforcement learning model based on attention mechanism (DREAM) to predict missing elements in the future. |
Shangfei Zheng; Hongzhi Yin; Tong Chen; Quoc Viet Hung Nguyen; Wei Chen; Lei Zhao; |
| 152 | Dynamic Graph Evolution Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework for generating satisfying recommendations in dynamic environments, called Dynamic Graph Evolution Learning (DGEL). |
Haoran Tang; Shiqing Wu; Guandong Xu; Qing Li; |
| 153 | Causal Decision Transformer for Recommender Systems Via Offline Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new model named the causal decision transformer for recommender systems (CDT4Rec). |
Siyu Wang; Xiaocong Chen; Dietmar Jannach; Lina Yao; |
| 154 | SCHash: Speedy Simplicial Complex Neural Networks Via Randomized Hashing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and speedy graph embedding algorithm dubbed SCHash. |
Xuan Tan; Wei Wu; Chuan Luo; |
| 155 | A Critical Reexamination of Intra-List Distance and Dispersion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we revisit the most popular diversity objective called intra-list distance (ILD), defined as the average pairwise distance between selected items, and a similar but lesser known objective called dispersion, which is the minimum pairwise distance. |
Naoto Ohsaka; Riku Togashi; |
| 156 | Contrastive Learning for Signed Bipartite Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Signed Bipartite Graph Contrastive Learning (SBGCL) method to learn robust node representation while retaining the implicit relations between nodes of the same type. |
Zeyu Zhang; Jiamou Liu; Kaiqi Zhao; Song Yang; Xianda Zheng; Yifei Wang; |
| 157 | It’s Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by this analysis, we propose simple-yet-effective linear autoencoder models using diagonal inequality constraints, called Relaxed Linear AutoEncoder (RLAE) and Relaxed Denoising Linear AutoEncoder (RDLAE). |
Jaewan Moon; Hye-young Kim; Jongwuk Lee; |
| 158 | Uncertainty Quantification for Extreme Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to investigate general uncertainty quantification approaches for tree-based XMC models with a probabilistic ensemble-based framework. |
Jyun-Yu Jiang; Wei-Cheng Chang; Jiong Zhang; Cho-Jui Hsieh; Hsiang-Fu Yu; |
| 159 | Distillation-Enhanced Graph Masked Autoencoders for Bundle Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, by conducting statistical analysis, we find that the auxiliary U-I information is far underexplored due to the following reasons: 1) Loosely combining the predicted results cannot well synthesize the knowledge from both views. 2) The local U-B and U-I collaborative relations might not be consistent, leading to GNN’s inaccurate modeling of user’s bundle preference from the U-I graph. 3) The U-I interactions are usually modeled equally while the significant ones corresponding to user’s bundle preference are less emphasized. Based on these analyses, we propose a Distillation-enhanced Graph Masked AutoEncoder (DGMAE) for bundle recommendation. |
Yuyang Ren; Zhang Haonan; Luoyi Fu; Xinbing Wang; Chenghu Zhou; |
| 160 | Candidate-aware Graph Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve the problems in GCL-based methods, we propose a novel method, Candidate-aware Graph Contrastive Learning for Recommendation, called CGCL. |
Wei He; Guohao Sun; Jinhu Lu; Xiu Susie Fang; |
| 161 | Graph Transformer for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. |
Chaoliu Li; Lianghao Xia; Xubin Ren; Yaowen Ye; Yong Xu; Chao Huang; |
| 162 | Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To reveal the real vulnerability of FedRecs, in this paper, we present a new poisoning attack method to manipulate target items’ ranks and exposure rates effectively in the top-K recommendation without relying on any prior knowledge. |
Wei Yuan; Quoc Viet Hung Nguyen; Tieke He; Liang Chen; Hongzhi Yin; |
| 163 | Topic-oriented Adversarial Attacks Against Black-box Neural Ranking Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on a more general type of perturbation and introduce the topic-oriented adversarial ranking attack task against NRMs, which aims to find an imperceptible perturbation that can promote a target document in ranking for a group of queries with the same topic. |
Yu-An Liu; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Wei Chen; Yixing Fan; Xueqi Cheng; |
| 164 | RCENR: A Reinforced and Contrastive Heterogeneous Network Reasoning Model for Explainable News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, brute-force pre-processing approaches used in conventional methods are not suitable for fast-changing news recommendation. Therefore, we propose an explainable news recommendation model: the Reinforced and Contrastive Heterogeneous Network Reasoning Model for Explainable News Recommendation (RCENR), consisting of NHN-R2 and MR&CO frameworks. |
Hao Jiang; Chuanzhen Li; Juanjuan Cai; Jingling Wang; |
| 165 | Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB). |
Chenguang Du; Kaichun Yao; Hengshu Zhu; Deqing Wang; Fuzhen Zhuang; Hui Xiong; |
| 166 | A Lightweight Constrained Generation Alternative for Query-focused Summarization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these approaches often require extra parameters & training, and generalize poorly to new dataset distributions. To mitigate this, we propose leveraging a recently developed constrained generation model Neurological Decoding (NLD) as an alternative to current QFS regimes which rely on additional sub-architectures and training. |
Zhichao Xu; Daniel Cohen; |
| 167 | A Mathematical Word Problem Generator with Structure Planning and Knowledge Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we draw inspiration from the human problem-designing process and propose a Mathematical structure Planning and Knowledge enhanced Generation model (MaPKG), following the "plan-then-generate" steps. |
Longhu Qin; Jiayu Liu; Zhenya Huang; Kai Zhang; Qi Liu; Binbin Jin; Enhong Chen; |
| 168 | Mixup-based Unified Framework to Overcome Gender Bias Resurgence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To eliminate undesired stereotyped associations in PLMs during fine-tuning, we present a mixup-based framework Mix-Debias from a new unified perspective, which directly combines debiasing PLMs with fine-tuning applications. |
Liu Yu; Yuzhou Mao; Jin Wu; Fan Zhou; |
| 169 | A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recommender system (RS) is widely applied in a multitude of scenarios to aid individuals obtaining the information they require efficiently. At the same time, the prevalence of popularity bias in such systems has become a widely acknowledged issue. To address this challenge, we propose a novel method named Model-Agnostic Popularity Debias Training Framework (MDTF). |
Fan Zhang; Qijie Shen; |
| 170 | A Simple Yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we argue that two kinds of gaps, i.e., domain gap and objective gap, hinder the transfer of knowledge from pre-training language models (PLMs) to ABSA tasks. |
Zengzhi Wang; Qiming Xie; Rui Xia; |
| 171 | A Static Pruning Study on Sparse Neural Retrievers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these sparse neural retrievers have been shown to increase the computational costs and latency of query processing compared to their classical counterparts. To mitigate this, we apply a well-known family of techniques for boosting the efficiency of query processing over inverted indexes: static pruning. |
Carlos Lassance; Simon Lupart; Hervé Déjean; Stéphane Clinchant; Nicola Tonellotto; |
| 172 | A Unified Formulation for The Frequency Distribution of Word Frequencies Using The Inverse Zipf’s Law Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, comparatively less attention has been paid to the investigation of the case of word frequencies. In this paper, we derive its analytical expression from the inverse of the underlying rank-size distribution as a function of total word count, vocabulary size and the shape parameter, thereby providing a unified framework to explain the nonlinear behavior of low frequencies on the log-log scale. |
Can Özbey; Talha Çolakoğlu; M. Şafak Bilici; Ekin Can Erkuş;; |
| 173 | Adapting Learned Sparse Retrieval for Long Documents Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We investigate existing aggregation approaches for adapting LSR to longer documents and find that proximal scoring is crucial for LSR to handle long documents. To leverage this property, we proposed two adaptations of the Sequential Dependence Model (SDM) to LSR: ExactSDM and SoftSDM. |
Thong Nguyen; Sean MacAvaney; Andrew Yates; |
| 174 | ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, this pre-defined data partitioning process heavily relies on prior knowledge, and it may neglect the underlying data distribution of each scenario, hence limiting the model’s representation capability. Regarding the above issues, we propose Adaptive Distribution Learning (ADL): an end-to-end optimization distribution framework which is composed of a clustering process and classification process. |
Jinyun Li; Jinyun Li; Jinyun Li; Huiwen Zheng; Yuanlin Liu; Minfang Lu; Lixia Wu; Haoyuan Hu; |
| 175 | Adversarial Meta Prompt Tuning for Open Compound Domain Adaptive Intent Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Concretely, we propose a meta prompt tuning method, which utilizes language prompts to elicit rich knowledge from large-scale pre-trained language models (PLMs) and automatically finds better prompt initialization that facilitates fast adaptation via meta learning. |
Feiteng Fang; Min Yang; Chengming Li; Ruifeng Xu; |
| 176 | Affective Relevance: Inferring Emotional Responses Via FNIRS Neuroimaging Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we consider the emotional response decoded directly from the human brain as an alternative dimension of relevance. |
Tuukka Ruotsalo; Kalle Mäkelä; Michiel M. Spapé; Luis A. Leiva; |
| 177 | Popularity Debiasing from Exposure to Interaction in Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a new criterion for popularity debiasing, i.e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion. |
Yuanhao Liu; Qi Cao; Huawei Shen; Yunfan Wu; Shuchang Tao; Xueqi Cheng; |
| 178 | Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conventional strategies for catastrophic forgetting are challenging to deploy due to memory constraints and diverse data distributions. To address this, we propose a novel drift-aware incremental learning framework based on ensemble learning for CTR prediction, which uses explicit error-based drift detection on streaming data to strengthen well-adapted ensembles and freeze ensembles that do not match the input distribution, avoiding catastrophic interference. |
Congcong Liu; Fei Teng; Xiwei Zhao; Zhangang Lin; Jinghe Hu; Jingping Shao; |
| 179 | Attacking Pre-trained Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a novel framework for backdoor attacking in pre-trained recommendation. |
Yiqing Wu; Ruobing Xie; Zhao Zhang; Yongchun Zhu; Fuzhen Zhuang; Jie Zhou; Yongjun Xu; Qing He; |
| 180 | Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel and effective aTtention-guided Multi-step FUsion Network for multimodal recommendation, named TMFUN. |
Yan Zhou; Jie Guo; Hao Sun; Bin Song; Fei Richard Yu; |
| 181 | Attention Mixtures for Time-Aware Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing architectures often overlook the complex dependencies between user preferences and the temporal context. In this short paper, we introduce MOJITO, an improved Transformer sequential recommender system that addresses this limitation. |
Viet Anh Tran; Guillaume Salha-Galvan; Bruno Sguerra; Romain Hennequin; |
| 182 | Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, rather than using more cross-lingual data for training, we propose to use cross-lingual query generation to augment passage representations with queries in languages other than the original passage language. |
Shengyao Zhuang; Linjun Shou; Guido Zuccon; |
| 183 | AutoDPQ: Automated Differentiable Product Quantization for Embedding Compression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the AutoDPQ framework, which automatically compacts low-frequency feature embeddings for each feature field to an adaptive magnitude. |
Xin Gan; Yuhao Wang; Xiangyu Zhao; Wanyu Wang; Yiqi Wang; Zitao Liu; |
| 184 | Bayesian Knowledge-driven Critiquing with Indirect Evidence Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to increase the flexibility of critique-based recommendation by integrating KGs and propose a novel Bayesian inference framework that enables reasoning with relational knowledge-based feedback. |
Armin Toroghi; Griffin Floto; Zhenwei Tang; Scott Sanner; |
| 185 | Behavior Modeling for Point of Interest Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A good understanding of user behavior is well-recognized as a key to develop effective user models and retrieval models to improve the search quality. Therefore, in this paper, we propose to investigate user behavior in POI search with a lab study in which users’ eye movements and their implicit feedback on the SERP are collected. |
Haitian Chen; Qingyao Ai; Zhijing Wu; Zhihong Wang; Yiqun Liu; Min Zhang; Shaoping Ma; Juan Hu; Naiqiang Tan; Hua Chai; |
| 186 | Benchmarking Middle-Trained Language Models for Neural Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose in this paper a benchmark of CoCondenser, RetroMAE, and LexMAE, under the same finetuning conditions. |
Hervé Déjean; Stephane Clinchant; Carlos Lassance; Simon Lupart; Thibault Formal; |
| 187 | BioAug: Conditional Generation Based Data Augmentation for Low-Resource Biomedical NER Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present BioAug, a novel data augmentation framework for low-resource BioNER. |
Sreyan Ghosh; Utkarsh Tyagi; Sonal Kumar; Dinesh Manocha; |
| 188 | BKD: A Bridge-based Knowledge Distillation Method for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel knowledge distillation approach called Bridge-based Knowledge Distillation (BKD), which employs a bridge model to facilitate the student model’s learning from the teacher model’s latent representations. |
Yin Deng; Yingxin Chen; Xin Dong; Lingchao Pan; Hai Li; Lei Cheng; Linjian Mo; |
| 189 | Calibration Learning for Few-shot Novel Product Description Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional approaches rely on large labelled datasets, which are often unavailable for novel products with limited data. To address this issue, we propose a calibration learning approach for few-shot novel product description. |
Zheng Liu; Mingjing Wu; Bo Peng; Yichao Liu; Qi Peng; Chong Zou; |
| 190 | Can Generative LLMs Create Query Variants for Test Collections? An Exploratory Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper explores the utility of a Large Language Model (LLM) to automatically generate queries and query variants from a description of an information need. |
Marwah Alaofi; Luke Gallagher; Mark Sanderson; Falk Scholer; Paul Thomas; |
| 191 | Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. |
Siyu Wang; Xiaocong Chen; Quan Z. Sheng; Yihong Zhang; Lina Yao; |
| 192 | CEC: Towards Learning Global Optimized Recommendation Through Causality Enhanced Conversion Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Among the research on the recommendation entry, most of them focus on improving the conversion volumes merely in the recommendation entry. However, such way could not ensure an increase in the global conversion volumes of the e-commerce platform. To achieve this goal by optimizing the recommendation entry only, in this paper, we focus on modeling the causality between the recommendation-entry-impression and the conversion by proposing the two-stage Causality Enhanced Conversion (CEC) model. |
Ran Le; Guo-qing Jiang; Xiufeng Shu; Ruidong Han; Qianzhong Li; Yacheng Li; Xiang Li; Wei Lin; |
| 193 | Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in ECommerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To jointly mitigate position bias in both item CTR and CVR prediction, we propose two position-bias-free CTR and CVR prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE). |
Yibo Wang; Yanbing Xue; Bo Liu; Musen Wen; Wenting Zhao; Stephen Guo; Philip S. Yu; |
| 194 | Computational Versus Perceived Popularity Miscalibration in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While popularity metrics provide a straightforward and well-defined means to measure popularity bias, it is unknown whether they actually reflect users’ perception of popularity bias. To address this research gap, we conduct a crowd-sourced user study on Prolific, involving 56 participants, to (1) investigate whether the level of perceived popularity miscalibration differs between common recommendation algorithms, (2) assess the correlation between perceived popularity miscalibration and its corresponding quantification according to a common offline metric. |
Oleg Lesota; Gustavo Escobedo; Yashar Deldjoo; Bruce Ferwerda; Simone Kopeinik; Elisabeth Lex; Navid Rekabsaz; Markus Schedl; |
| 195 | Connecting Unseen Domains: Cross-Domain Invariant Learning in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel framework that considers both CDR and domain generalization through a united causal invariant view. |
Yang Zhang; Yue Shen; Dong Wang; Jinjie Gu; Guannan Zhang; |
| 196 | ConQueR: Contextualized Query Reduction Using Search Logs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes Contextualized Query Reduction (ConQueR) using a pre-trained language model (PLM). |
Hye-young Kim; Minjin Choi; Sunkyung Lee; Eunseong Choi; Young-In Song; Jongwuk Lee; |
| 197 | Context-Aware Modeling Via Simulated Exposure Page for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although the user’s click action on an item will be affected by the other exposed items (called contextual items), current CTR prediction methods do not exploit this context because CTR prediction is performed before the contextual items are generated. This paper introduces a solution Contextual Items Simulation and Modeling (CISM) to tackle this limitation. |
Xiang Li; Shuwei Chen; Jian Dong; Jin Zhang; Yongkang Wang; Xingxing Wang; Dong Wang; |
| 198 | Contrastive Learning for Conversion Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the Contrastive Learning for CVR prediction (CL4CVR) framework. |
Wentao Ouyang; Rui Dong; Xiuwu Zhang; Chaofeng Guo; Jinmei Luo; Xiangzheng Liu; Yanlong Du; |
| 199 | Curriculum Modeling The Dependence Among Targets with Multi-task Learning for Financial Marketing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a prior information merged model (PIMM), which explicitly models the logical dependence among tasks with a novel prior information merged (PIM) module for multiple sequential dependence task learning in a curriculum manner. |
Yunpeng Weng; Xing Tang; Liang Chen; Xiuqiang He; |
| 200 | Decomposing Logits Distillation for Incremental Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To explicitly constrain each term, we propose a novel Decomposing Logits Distillation (DLD) method, enhancing the model’s ability to retain old knowledge and mitigate catastrophic forgetting. |
Duzhen Zhang; Yahan Yu; Feilong Chen; Xiuyi Chen; |
| 201 | Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose "Adversarial Image Denoiser" (AiD), a novel defense method that cleans up the item images by malicious perturbations. |
Felice Antonio Merra; Vito Walter Anelli; Tommaso Di Noia; Daniele Malitesta; Alberto Carlo Maria Mancino; |
| 202 | DeviceGPT: A Generative Pre-Training Transformer on The Heterogenous Graph for Internet of Things Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the rapid development of Internet technology, there are more and more meaningful applications for Internet devices, including device identification, geolocation and others, whose performance needs improvement. To replicate the several claimed successes of GNNs, this paper proposes DeviceGPT based on a generative pre-training transformer on a heterogeneous graph via self-supervised learning to learn interactions-rich information of devices from its large-scale databases well. |
Yimo Ren; Jinfa Wang; Hong Li; Hongsong Zhu; Limin Sun; |
| 203 | Dimension-Prompts Boost Commonsense Consolidation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But, such brute-force mixing inevitably hinders effective knowledge consolidation due to i) ambiguous, polysemic, and/or inconsistent relations across sources and ii) knowledge learned in an entangled manner despite distinct types (e.g., causal, temporal). To mitigate this, we adopt a concept of commonsense knowledge dimension and propose a brand-new dimension-disentangled knowledge model (D2KM) learning paradigm with multiple sources. |
Jiazhan Feng; Chongyang Tao; Tao Shen; Chang Liu; Dongyan Zhao; |
| 204 | Disentangling User Conversations with Voice Assistants for Online Shopping Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a multi-task learning approach with a contrastive learning objective, DiSC, to disentangle conversations between two speakers — a user and a virtual speech assistant, for a novel domain of e-commerce. |
Nikhita Vedula; Marcus Collins; Oleg Rokhlenko; |
| 205 | DocGraphLM: Documental Graph Language Model for Information Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce DocGraphLM, a novel framework that combines pre-trained language models with graph semantics. |
Dongsheng Wang; Zhiqiang Ma; Armineh Nourbakhsh; Kang Gu; Sameena Shah; |
| 206 | Edge-cloud Collaborative Learning with Federated and Centralized Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, our proposed Edge-Cloud Collaborative Knowledge Transfer Framework (ECCT) jointly utilizes the edge-side features and the cloud-side features, enabling bi-directional knowledge transfer between the two by sharing feature embeddings and prediction logits. |
Zexi Li; Qunwei Li; Yi Zhou; Wenliang Zhong; Guannan Zhang; Chao Wu; |
| 207 | SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces Sparsified Late Interaction for Multi-vector (SLIM) retrieval with inverted indexes. |
Minghan Li; Sheng-Chieh Lin; Xueguang Ma; Jimmy Lin; |
| 208 | Evaluating Cross-modal Generative Models Using Retrieval Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel cross-modal retrieval framework to evaluate the effectiveness of cross-modal (image-to-text and text-to-image) generative models using reference text and images. |
Shivangi Bithel; Srikanta Bedathur; |
| 209 | Event-Aware Adaptive Clustering Uplift Network for Insurance Creative Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a new recommendation problem, i.e., the Pop-up One-time Marketing (POM), where the product-tying marketing creative only pops up one time when the user pays for the main item. |
Wanjie Tao; Huihui Liu; Xuqi Li; Qun Dai; Hong Wen; Zulong Chen; |
| 210 | Examining The Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To investigate the extent to which controlled or uncontrolled (i.e., confounding) variables such as task sequence or duration influence the observed signals, we conducted a pilot study where each participant completed four types of information-processing activities (READ, LISTEN, SPEAK, and WRITE). |
Kaixin Ji; Damiano Spina; Danula Hettiachchi; Flora Dilys Salim; Falk Scholer; |
| 211 | Explain Like I Am BM25: Interpreting A Dense Model’s Ranked-List with A Sparse Approximation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: As a form of local per-query explanations, we introduce the notion of equivalent queries that are generated by maximizing the similarity between the NRM’s results and the result set of a sparse retrieval system with the equivalent query. |
Michael Llordes; Debasis Ganguly; Sumit Bhatia; Chirag Agarwal; |
| 212 | Exploiting Cluster-Skipping Inverted Index for Semantic Place Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, our contribution toward improving the efficiency of semantic place retrieval is two-fold. |
Enes Recep Cinar; Ismail Sengor Altingovde; |
| 213 | Exploiting Ubiquitous Mentions for Document-Level Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address it, we propose to incorporate coreferences (e.g. pronouns and common nouns) into mentions, based on which we refine and re-annotate the widely-used DocRED benchmark as R-DocRED. |
Ruoyu Zhang; Yanzeng Li; Minhao Zhang; Lei Zou; |
| 214 | Exploration of Unranked Items in Safe Online Learning to Re-Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a safe OLTR algorithm that efficiently exchanges one of the items in the current ranking with an item outside the ranking (i.e., an unranked item) to perform exploration. |
Hiroaki Shiino; Kaito Ariu; Kenshi Abe; Riku Togashi; |
| 215 | Fairness for Both Readers and Authors: Evaluating Summaries of User Generated Content Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we look at summarization of user-generated content as a two-sided problem where satisfaction of both readers and authors is crucial. |
Garima Chhikara; Kripabandhu Ghosh; Saptarshi Ghosh; Abhijnan Chakraborty; |
| 216 | Faster Dynamic Pruning Via Reordering of Documents in Inverted Indexes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we tailor approaches that reorder the documents in the inverted index based on their access counts and ranks for previous queries. |
Erman Yafay; Ismail Sengor Altingovde; |
| 217 | FINAL: Factorized Interaction Layer for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by factorization machines, in this paper, we propose FINAL, a factorized interaction layer that extends the widely-used linear layer and is capable of learning 2nd-order feature interactions. |
Jieming Zhu; Qinglin Jia; Guohao Cai; Quanyu Dai; Jingjie Li; Zhenhua Dong; Ruiming Tang; Rui Zhang; |
| 218 | Forget Me Now: Fast and Exact Unlearning in Neighborhood-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper focuses on "unlearning" such user data from neighborhood-based recommendation models on sparse, high-dimensional datasets. We present caboose, a custom top-k index for such models, which enables fast and exact deletion of user interactions. |
Sebastian Schelter; Mozhdeh Ariannezhad; Maarten de Rijke; |
| 219 | Friend Ranking in Online Games Via Pre-training Edge Transformers Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we treat friend recall as a link prediction problem and explore several link prediction methods which can use features of both active and lost players, as well as historical events. |
Liang Yao; Jiazhen Peng; Shenggong Ji; Qiang Liu; Hongyun Cai; Feng He; Xu Cheng; |
| 220 | Gated Attention with Asymmetric Regularization for Transformer-based Continual Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a transformer-based CGL method (Trans-CGL), thereby taking full advantage of the transformer’s properties to mitigate the TCF problem. |
Hongxiang Lin; Ruiqi Jia; Xiaoqing Lyu; |
| 221 | Generative Relevance Feedback with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead of building a language model from retrieved results, we propose Generative Relevance Feedback (GRF) that builds probabilistic feedback models from long-form text generated from Large Language Models. |
Iain Mackie; Shubham Chatterjee; Jeffrey Dalton; |
| 222 | Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a transference-driven approach CoGrad that adaptively maximizes knowledge transference via Coordinated Gradient modification. |
Xuanhua Yang; Jianxin Zhao; Shaoguo Liu; Liang Wang; Bo Zheng; |
| 223 | Graph Collaborative Signals Denoising and Augmentation for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a new graph adjacency matrix that incorporates user-user and item-item correlations, as well as a properly designed user-item interaction matrix that balances the number of interactions across all users. |
Ziwei Fan; Ke Xu; Zhang Dong; Hao Peng; Jiawei Zhang; Philip S. Yu; |
| 224 | Neighborhood-based Hard Negative Mining for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we observe that as training progresses, the distributions of node-pair similarities in different groups with varying degrees of neighborhood overlap change significantly, suggesting that item pairs in distinct groups may possess different negative relationships. |
Lu Fan; Jiashu Pu; Rongsheng Zhang; Xiao-Ming Wu; |
| 225 | Hierarchical Type Enhanced Negative Sampling for Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by type constraint methods, we propose Hierarchical Type Enhanced Negative Sampling (HTENS) which leverages hierarchical entity type information and entity-relation cooccurrence information to optimize the sampling probability distribution of negative samples. |
Zhenzhou Lin; Zishuo Zhao; Jingyou Xie; Ying Shen; |
| 226 | HiPrompt: Few-Shot Biomedical Knowledge Fusion Via Hierarchy-Oriented Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. |
Jiaying Lu; Jiaming Shen; Bo Xiong; Wenjing Ma; Steffen Staab; Carl Yang; |
| 227 | How Significant Attributes Are in The Community Detection of Attributed Multiplex Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we observe that real attributed multiplex networks exhibit two unique features, namely, consistency and homogeneity of node attributes. Therefore, in this paper, we propose a novel method, called ACDM, which is based on these two characteristics of attributes, to detect communities on attributed multiplex networks. |
Junwei Cheng; Chaobo He; Kunlin Han; Wenjie Ma; Yong Tang; |
| 228 | HyperFormer: Learning Expressive Sparse Feature Representations Via Hypergraph Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose to model the sparse features of different instances using hypergraphs where each node represents a data instance and each hyperedge denotes a distinct feature value. |
Kaize Ding; Albert Jiongqian Liang; Bryan Perozzi; Ting Chen; Ruoxi Wang; Lichan Hong; Ed H. Chi; Huan Liu; Derek Zhiyuan Cheng; |
| 229 | Best Prompts for Text-to-Image Models and How to Find Them Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we propose a human-in-the-loop method for discovering the most effective combination of prompt keywords using a genetic algorithm. |
Nikita Pavlichenko; Dmitry Ustalov; |
| 230 | Improved Vector Quantization For Dense Retrieval with Contrastive Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work combines the benefits of contrastive learning and distillation by using contrastive distillation whereby the teacher outputs contrastive scores that the student learns from. |
James O’ Neill; Sourav Dutta; |
| 231 | Improving Conversational Passage Re-ranking with View Ensemble Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. |
Jia-Huei Ju; Sheng-Chieh Lin; Ming-Feng Tsai; Chuan-Ju Wang; |
| 232 | Improving News Recommendation Via Bottlenecked Multi-task Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To solve it, we propose a bottlenecked multi-task pre-training approach, which relies on an information-bottleneck encoder-decoder architecture to compress the useful semantic information into the news embedding. |
Xiongfeng Xiao; Qing Li; Songlin Liu; Kun Zhou; |
| 233 | Inference at Scale: Significance Testing for Large Search and Recommendation Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we empirically study the behavior of significance tests with large search and recommendation evaluation data. |
Ngozi Ihemelandu; Michael D. Ekstrand; |
| 234 | LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Queries with similar information needs tend to have similar document clicks, especially in biomedical literature search engines where queries are generally short and top documents account for most of the total clicks. Motivated by this, we present a novel architecture for biomedical literature search, namely Log-Augmented DEnse Retrieval (LADER), which is a simple plug-in module that augments a dense retriever with the click logs retrieved from similar training queries. |
Qiao Jin; Andrew Shin; Zhiyong Lu; |
| 235 | LAPCA: Language-Agnostic Pretraining with Cross-Lingual Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While previous works used machine translation and iterative training, we present a novel approach to cross-lingual pretraining called LAPCA (language-agnostic pretraining with cross-lingual alignment). We train the LAPCA-LM model based on XLM-RoBERTa and łexa that significantly improves cross-lingual knowledge transfer for question answering and sentence retrieval on, e.g., XOR-TyDi and Mr. TyDi datasets, and in the zero-shot cross-lingual scenario performs on par with supervised methods, outperforming many of them on MKQA. |
Dmitry Abulkhanov; Nikita Sorokin; Sergey Nikolenko; Valentin Malykh; |
| 236 | Learning from Crowds with Annotation Reliability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate the reliability of annotations to improve learning from crowds. |
Zhi Cao; Enhong Chen; Ye Huang; Shuanghong Shen; Zhenya Huang; |
| 237 | Learning Through Interpolative Augmentation of Dynamic Curvature Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose DynaMix, a framework that automatically selects an example-specific geometry and performs Mixup between the different geometries to improve training dynamics and generalization. |
Parth Chhabra; Atula Tejaswi Neerkaje; Shivam Agarwal; Ramit Sawhney; Megh Thakkar; Preslav Nakov; Sudheer Chava; |
| 238 | Learning to Ask Clarification Questions with Spatial Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite the extensive applications of spatial information grounded dialogues, it remains an understudied area on learning to ask clarification questions with the capability of spatial reasoning. In this work, we propose a novel method, named SpatialCQ, for this problem. |
Yang Deng; Shuaiyi Li; Wai Lam; |
| 239 | Learning to Ask Questions for Zero-shot Dialogue State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a method for performing zero-shot Dialogue State Tracking (DST) by casting the task as a learning-to-ask-questions framework. |
Diogo Tavares; David Semedo; Alexander Rudnicky; Joao Magalhaes; |
| 240 | Limitations of Open-Domain Question Answering Benchmarks for Document-level Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this approach ignores important document-level cues that can be crucial in answering questions. This paper reviews three open-domain QA benchmarks from a document-level perspective and finds that they are biased towards passage-level information. |
Ehsan Kamalloo; Charles L. A. Clarke; Davood Rafiei; |
| 241 | LogicRec: Recommendation with Users’ Logical Requirements Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we formulate the problem of recommendation with users’ logical requirements (LogicRec) and construct benchmark datasets for LogicRec. |
Zhenwei Tang; Griffin Floto; Armin Toroghi; Shichao Pei; Xiangliang Zhang; Scott Sanner; |
| 242 | Look Ahead: Improving The Accuracy of Time-Series Forecasting By Previewing Future Time Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate a novel direction towards improving the forecasting performance even more, which is orthogonal to the aforementioned mainstreams as a model-agnostic scheme. |
Seonmin Kim; Dong-Kyu Chae; |
| 243 | LOVF: Layered Organic View Fusion for Click-through Rate Prediction in Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of utilizing data from organic recommendation to reinforce click-through rate prediction in advertising scenarios from a multi-view learning perspective. |
Lingwei Kong; Lu Wang; Xiwei Zhao; Junsheng Jin; Zhangang Lin; Jinghe Hu; Jingping Shao; |
| 244 | MA-MRC: A Multi-answer Machine Reading Comprehension Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to construct an MRC dataset with both data of single answer and multiple answers. |
Zhiang Yue; Jingping Liu; Cong Zhang; Chao Wang; Haiyun Jiang; Yue Zhang; Xianyang Tian; Zhedong Cen; Yanghua Xiao; Tong Ruan; |
| 245 | Matching Point of Interests and Travel Blog with Multi-view Information Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we propose a novel end-to-end framework for travel blogs ranking, coined Matching POI and Travel Blogs with Multi-view InFormation (MOTIF). |
Shuokai Li; Jingbo Zhou; Jizhou Huang; Hao Chen; Fuzhen Zhuang; Qing He; Dejing Dou; |
| 246 | MaxSimE: Explaining Transformer-based Semantic Similarity Via Contextualized Best Matching Token Pairs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose MaxSimE, an explanation method for language models applied to measure semantic similarity. |
Eduardo Brito; Henri Iser; |
| 247 | MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose a framework namedMulti-Distribution Data Learning (MDDL) to address the challenge of effectively utilizing both strategy and random data for training RL models on mixed multi-distribution data. |
Xiaowen Shi; Ze Wang; Yuanying Cai; Xiaoxu Wu; Fan Yang; Guogang Liao; Yongkang Wang; Xingxing Wang; Dong Wang; |
| 248 | MDKG: Graph-Based Medical Knowledge-Guided Dialogue Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This makes the systems more prone to misdiagnosis in case of diseases with limited information. To overcome this issue, we present MDKG, an end-to-end dialogue system for medical dialogue generation (MDG) specifically designed to adapt to new diseases by quickly learning and evolving a meta-knowledge graph that allows it to reason about disease-symptom correlations. |
Usman Naseem; Surendrabikram Thapa; Qi Zhang; Liang Hu; Mehwish Nasim; |
| 249 | Measuring Service-Level Learning Effects in Search Via Query-Randomized Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, for experiments targeting improvements to the behavior data available to such features (e.g. online exploration), this pathway is precisely the one we are trying to affect; if such changes occur identically in treatment and control, then they cannot be measured. To address this, we propose the use of experiments which instead randomize traffic based on the search query. |
Paul Musgrave; Cuize Han; Parth Gupta; |
| 250 | Mining Interest Trends and Adaptively Assigning Sample Weight for Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we model users’ instant interest based on their present behavior and all their previous behaviors. |
Kai Ouyang; Xianghong Xu; Miaoxin Chen; Zuotong Xie; Hai-Tao Zheng; Shuangyong Song; Yu Zhao; |
| 251 | Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Then to handle the stochastic feedback more efficiently, we design two stochastic reward stabilization frameworks that replace the direct stochastic feedback with that learned by a supervised model. Both frameworks are model-agnostic, i.e., they can effectively utilize various supervised models. |
Tianchi Cai; Shenliao Bao; Jiyan Jiang; Shiji Zhou; Wenpeng Zhang; Lihong Gu; Jinjie Gu; Guannan Zhang; |
| 252 | Modeling Orders of User Behaviors Via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach. |
Menghan Wang; Jinming Yang; Yuchen Guo; Yuming Shen; Mengying Zhu; Yanlin Wang; |
| 253 | Multi-Grained Topological Pre-Training of Language Models in Sponsored Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Multi-Grained Topological Pre-Training paradigm, MGTLM, to teach language models to understand multi-grained topological information in behavior graphs, which contributes to eliminating explicit graph aggregations and avoiding information loss. |
Zhoujin Tian; Chaozhuo Li; Zhiqiang Zuo; Zengxuan Wen; Xinyue Hu; Xiao Han; Haizhen Huang; Senzhang Wang; Weiwei Deng; Xing Xie; Qi Zhang; |
| 254 | Multi-grained Representation Learning for Cross-modal Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, single-grained representation is difficult to solve the situation that an audio is described by multiple texts of different granularity levels, because the association pattern between audio and text is complex. Therefore, we propose an adaptive aggregation strategy to automatically find the optimal pool function to aggregate the features into a comprehensive representation, so as to learn valuable multi-grained representation. |
Shengwei Zhao; Linhai Xu; Yuying Liu; Shaoyi Du; |
| 255 | Multiple Topics Community Detection in Attributed Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since existing methods are often not effective to detect communities with multiple topics in attributed networks, we propose a method named SSAGCN via Autoencoder-style self-supervised learning. |
Chaobo He; Junwei Cheng; Guohua Chen; Yong Tang; |
| 256 | NC2T: Novel Curriculum Learning Approaches for Cross-Prompt Trait Scoring Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As evaluating and scoring essays based on complex traits is costly and time-consuming, datasets for such AES evaluations are limited. To address these issues, we developed a cross-prompt trait scoring AES model and proposed a suitable curriculum learning (CL) design. |
Yejin Lee; Seokwon Jeong; Hongjin Kim; Tae-il Kim; Sung-Won Choi; Harksoo Kim; |
| 257 | Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the application of pseudo relevance feedback (PRF) to further enhance retrieval effectiveness results in a doubling of online latency. To address this challenge, this paper presents a single-pass dense retrieval framework that shifts the PRF process offline through the utilization of pre-generated pseudo-queries. |
Xueru Wen; Xiaoyang Chen; Xuanang Chen; Ben He; Le Sun; |
| 258 | On Answer Position Bias in Transformers for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we analyze the self-attention and embedding generation components of five Transformer-based models with different architectures and position embedding strategies. |
Rafael Glater; Rodrygo L. T. Santos; |
| 259 | On The Effects of Regional Spelling Conventions in Retrieval Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate and quantify how well various ranking models perform in a clear-cut case of synonymity: when words are simply expressed in different surface forms due to regional differences in spelling conventions (e.g., color vs colour). |
Andreas Chari; Sean MacAvaney; Iadh Ounis; |
| 260 | On The Impact of Data Quality on Image Classification Fairness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We describe noise in the labels as inaccuracies in the labelling of the data in the training set and noise in the data as distortions in the data, also in the training set. |
Aki Barry; Lei Han; Gianluca Demartini; |
| 261 | One-Shot Labeling for Automatic Relevance Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Holes can reduce the apparent effectiveness of retrieval systems during evaluation and introduce biases in models trained with incomplete data. In this work, we explore whether large language models can help us fill such holes to improve offline evaluations. |
Sean MacAvaney; Luca Soldaini; |
| 262 | Optimizing Reciprocal Rank with Bayesian Average for Improved Next Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel objective function, namely Adjusted-RR, to directly optimize Mean Reciprocal Rank. |
Xiangkui Lu; Jun Wu; Jianbo Yuan; |
| 263 | Patterns of Gender-Specializing Query Reformulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study a special category of query reformulations that involve specifying demographic group attributes, such as gender, as part of the reformulated query (e.g., ”olympic 2021 soccer results” -> ”olympic 2021 women’s soccer results"). |
Amifa Raj; Bhaskar Mitra; Nick Craswell; Michael Ekstrand; |
| 264 | Personalized Dynamic Recommender System for Investors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike representative areas of recommendation research such as e-commerce platforms where items’ features are fixed, in investment scenarios financial instruments’ features such as stock price, also change dynamically over time. To capture these dynamic features and provide a better-personalized recommendation for amateur investors, this study proposes a Personalized Dynamic Recommender System for Investors, PDRSI. |
Takehiro Takayanagi; Chung-Chi Chen; Kiyoshi Izumi; |
| 265 | Personalized Showcases: Generating Multi-Modal Explanations for Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to further enrich explanations, we propose a new task named personalized showcases, in which we provide both textual and visual information to explain our recommendations. |
An Yan; Zhankui He; Jiacheng Li; Tianyang Zhang; Julian McAuley; |
| 266 | PersonalTM: Transformer Memory for Personalized Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The memorization power of DSI framework makes it suitable for personalized retrieval tasks. Therefore, we propose a Personal Transformer Memory (PersonalTM) architecture for personalized text retrieval. |
Ruixue Lian; Sixing Lu; Clint Solomon; Gustavo Aguilar; Pragaash Ponnusamy; Jialong Han; Chengyuan Ma; Chenlei Guo; |
| 267 | PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training Via Prompting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To further reduce the amount of supervision, we propose Prompts-in-The-Loop (PiTL) that prompts knowledge from large language models (LLMs) to describe images. |
Zixin Guo; Tzu-Jui Julius Wang; Selen Pehlivan; Abduljalil Radman; Jorma Laaksonen; |
| 268 | Power Norm Based Lifelong Learning for Paraphrase Generations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel framework name "power norm based lifelong learning" (PNLLL), which aims to remedy the catastrophic forgetting issues with a power normalization on NLP transformer models. |
Dingcheng Li; Peng Yang; Yue Zhang; Ping Li; |
| 269 | Prediction Then Correction: An Abductive Prediction Correction Method for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, there are inherent gaps between testing and training data, which can make this approach unreliable. To address this issue, we propose an Abductive Prediction Correction (APC) framework for sequential recommendation. |
Yulong Huang; Yang Zhang; Qifan Wang; Chenxu Wang; Fuli Feng; |
| 270 | Priming and Actions: An Analysis in Conversational Search Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we employed the concept of Priming effects from the Psychology literature to identify core stimuli for each abstracted effect. |
Xiao Fu; Aldo Lipani; |
| 271 | Private Meeting Summarization Without Performance Loss Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower performance on in-sample data, differential privacy improves performance when evaluated on unseen meeting types. |
Seolhwa Lee; Anders Søgaard; |
| 272 | Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To mitigate the issue, we propose a simple yet effective prompt learning approach that can preserve the multilinguality of multilingual pre-trained language model (mPLM) in FS-XLT and MTL by bridging the gap between pre-training and fine-tuning with Fixed-prompt LM Tuning and our hand-crafted prompts. |
Lei Liu; Jimmy Xiangji Huang; |
| 273 | Quantifying and Leveraging User Fatigue for Interventions in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose detecting early signs of users losing interest, allowing time for intervention, and introduce a new formulation ofuser fatigue as short-term dissatisfaction, providing early signals to predict long-term churn. |
Hitesh Sagtani; Madan Gopal Jhawar; Akshat Gupta; Rishabh Mehrotra; |
| 274 | Quantifying Ranker Coverage of Different Query Subspaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Task Subspace Coverage (TaSC /tAHsk/) metric, which systematically quantifies whether and to what extent improvements in retrieval effectiveness happen on similar or disparate query subspaces for different rankers. |
Negar Arabzadeh; Amin Bigdeli; Radin Hamidi Rad; Ebrahim Bagheri; |
| 275 | Query-specific Variable Depth Pooling Via Query Performance Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, in this paper we argue that the annotation effort can be substantially reduced if the depth of the pool is made a variable quantity for each query, the rationale being that the number of documents relevant to the information need can widely vary across queries. |
Debasis Ganguly; Emine Yilmaz; |
| 276 | RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing attempts usually formulate text ranking as a classification problem and rely on postprocessing to obtain a ranked list. In this paper, we propose RankT5 and study two T5-based ranking model structures, an encoder-decoder and an encoder-only one, so that they not only can directly output ranking scores for each query-document pair, but also can be fine-tuned with pairwise or listwise ranking losses to optimize ranking performance. |
Honglei Zhuang; Zhen Qin; Rolf Jagerman; Kai Hui; Ji Ma; Jing Lu; Jianmo Ni; Xuanhui Wang; Michael Bendersky; |
| 277 | Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose TB-Rater, a Transformer model which combines conversational-flow features with user behavior features for predicting user ratings in a CTA scenario. |
Rafael Ferreira; David Semedo; João Magalhães; |
| 278 | Read It Twice: Towards Faithfully Interpretable Fact Verification By Revisiting Evidence Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although existing approaches leverage the similarity measure of semantic or surface form between claims and documents to retrieve evidence, they all rely on certain heuristics that prevent them from satisfying all three requirements. In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i.e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy. |
Xuming Hu; Zhaochen Hong; Zhijiang Guo; Lijie Wen; Philip Yu; |
| 279 | Reducing Spurious Correlations for Relation Extraction By Feature Decomposition and Semantic Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a spurious correlation reduction method for RE via feature decomposition and semantic augmentation (denoted as FDSA). |
Tianshu Yu; Min Yang; Chengming Li; Ruifeng Xu; |
| 280 | Representation Sparsification with Hybrid Thresholding for Fast SPLADE-based Document Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper describes a representation sparsification scheme based on hard and soft thresholding with an inverted index approximation for faster SPLADE-based document retrieval. |
Yifan Qiao; Yingrui Yang; Shanxiu He; Tao Yang; |
| 281 | Retrieval-Enhanced Generative Model for Large-Scale Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, inference with these methods relies solely on the model parameters for implicit reasoning and neglects the use of KG itself, which limits the performance since the model lacks the capacity to memorize a vast number of triplets. To tackle this issue, we introduce ReSKGC, a Retrieval-enhanced Seq2seq KGC model, which selects semantically relevant triplets from the KG and uses them as evidence to guide output generation with explicit reasoning. |
Donghan Yu; Yiming Yang; |
| 282 | Review-based Multi-intention Contrastive Learning for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, the important intentions contained in user reviews are not modeled effectively. In order to solve the above problems, we propose a novel Review-based Multi-intention Contrastive Learning (RMCL) method. |
Wei Yang; Tengfei Huo; Zhiqiang Liu; Chi Lu; |
| 283 | RewardTLG: Learning to Temporally Language Grounding from Flexible Reward Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the training process of chatGPT, we innovatively adopt a vision-language pre-training (VLP) model as a reward model, which provides flexible rewards to help the localization-based TLG task converge. |
Yawen Zeng; Keyu Pan; Ning Han; |
| 284 | Robust Causal Inference for Recommender System to Overcome Noisy Confounders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these estimators often assume that confounders are precisely observable, which is not always the case in real-world scenarios. To address this challenge, we propose a novel method called Adversarial Training-based IPS (AT-IPS), which uses adversarial training to handle noisy confounders. |
Zhiheng Zhang; Quanyu Dai; Xu Chen; Zhenhua Dong; Ruiming Tang; |
| 285 | Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we characterize how presentation bias affects different layout options, which present the results in column- or row-wise fashion. |
Patrik Dokoupil; Ladislav Peska; Ludovico Boratto; |
| 286 | Searching for Products in Virtual Reality: Understanding The Impact of Context and Result Presentation on User Experience Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study two factors that influence users’ experience when shopping in VR through voice queries: (1) context alignment of the search environment and (2) the level of detail on the Search Engine Results Page (SERP). |
Austin Ward; Sandeep Avula; Hao-Fei Cheng; Sheikh Muhammad Sarwar; Vanessa Murdock; Eugene Agichtein; |
| 287 | SelfLRE: Self-refining Representation Learning for Low-resource Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we propose a novel LRE architecture named SelfLRE, which leverages two complementary modules, one module uses self-training to obtain pseudo-labels for unlabeled data, and the other module uses self-ensembling learning to obtain the task-agnostic representations, and leverages the existing pseudo-labels to refine the better task-specific representations on unlabeled data. |
Xuming Hu; Junzhe Chen; Shiao Meng; Lijie Wen; Philip S. Yu; |
| 288 | Sharpness-Aware Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we propose an effective training schema, called gSAM, under the principle that theflatter minima has a better generalization ability than thesharper ones. |
Huiyuan Chen; Chin-Chia Michael Yeh; Yujie Fan; Yan Zheng; Junpeng Wang; Vivian Lai; Mahashweta Das; Hao Yang; |
| 289 | Simple Approach for Aspect Sentiment Triplet Extraction Using Span-Based Segment Tagging and Dual Extractors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These solutions suffer from: 1) high dependency on custom bidirectional structures, 2) inadequate representation of the information through existing tagging schemes, and 3) insufficient usage of all available sentiment data. To address the above issues, we propose a simple span-based solution named SimSTAR with Segment Tagging And dual extRactors. |
Dongxu Li; Zhihao Yang; Yuquan Lan; Yunqi Zhang; Hui Zhao; Gang Zhao; |
| 290 | Simpler Is Much Faster: Fair and Independent Inner Product Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To scale well to large datasets, we propose a simple yet efficient algorithm that runs in O(log n + k) expected time. |
Kazuyoshi Aoyama; Daichi Amagata; Sumio Fujita; Takahiro Hara; |
| 291 | Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present a unified framework for news recommendation, allowing for a systematic and fair comparison of news recommenders across several crucial design dimensions: (i) candidate-awareness in user modeling, (ii) click behavior fusion, and (iii) training objectives. |
Andreea Iana; Goran Glavas; Heiko Paulheim; |
| 292 | SimTDE: Simple Transformer Distillation for Sentence Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we introduce SimTDE, a simple knowledge distillation framework to compress sentence embeddings transformer models with minimal performance loss and significant size and latency reduction. |
Jian Xie; Xin He; Jiyang Wang; Zimeng Qiu; Ali Kebarighotbi; Farhad Ghassemi; |
| 293 | Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work introduces a new postprocessing approach based on Sinkhorn transformations that outperforms DSL. |
Konstantin Yakovlev; Gregory Polyakov; Ilseyar Alimova; Alexander Podolskiy; Andrey Bout; Sergey Nikolenko; Irina Piontkovskaya; |
| 294 | SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we combine the strengths of both the sparse and dense representations for first-stage retrieval. |
Weize Kong; Jeffrey M. Dudek; Cheng Li; Mingyang Zhang; Michael Bendersky; |
| 295 | Surprise: Result List Truncation Via Extreme Value Theory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose Surprise scoring, a statistical method that leverages the Generalized Pareto Distribution that arises in extreme value theory to produce interpretable and calibrated relevance scores at query time using nothing more than the ranked scores. |
Dara Bahri; Che Zheng; Yi Tay; Donald Metzler; Andrew Tomkins; |
| 296 | ExaRanker: Synthetic Explanations Improve Neural Rankers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent work has shown that incorporating explanations into the output generated by large language models (LLMs) can significantly enhance performance on a broad spectrum of reasoning tasks. Our study extends these findings by demonstrating the benefits of explanations for neural rankers. |
Fernando Ferraretto; Thiago Laitz; Roberto Lotufo; Rodrigo Nogueira; |
| 297 | TAML: Time-Aware Meta Learning for Cold-Start Problem in News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Time-Aware Meta-Learning (TAML), a novel framework that focuses on cold-start users in news recommendation systems. |
Jingyuan Li; Yue Zhang; Xuan Lin; Xinxing Yang; Ge Zhou; Longfei Li; Hong Chen; Jun Zhou; |
| 298 | Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel content-based text-to-motion retrieval task, which aims at retrieving relevant motions based on a specified natural-language textual description. |
Nicola Messina; Jan Sedmidubsky; Fabrizio Falchi; Tomás Rebok; |
| 299 | The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose H-CARS, a novel strategy to poison recommender systems via CFs. |
Ziheng Chen; Fabrizio Silvestri; Jia Wang; Yongfeng Zhang; Gabriele Tolomei; |
| 300 | The Tale of Two MSMARCO – and Their Unfair Comparisons Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the addition of titles actually leaks relevance information, while breaking the original guidelines of the MS MARCO-passage dataset. In this work, we investigate the differences between the two corpora and demonstrate empirically that they make a significant difference when evaluating a new method. |
Carlos Lassance; Stephane Clinchant; |
| 301 | Think Rationally About What You See: Continuous Rationale Extraction for Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. |
Xuming Hu; Zhaochen Hong; Chenwei Zhang; Irwin King; Philip Yu; |
| 302 | Towards Robust Knowledge Tracing Models Via K-Sparse Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In spite of the impressive performance achieved by these attentional DLKT models, many of them are often vulnerable to run the risk of overfitting, especially on small-scale educational datasets. Therefore, in this paper, we propose sparseKT, a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. |
Shuyan Huang; Zitao Liu; Xiangyu Zhao; Weiqi Luo; Jian Weng; |
| 303 | TripSafe: Retrieving Safety-related Abnormal Trips in Real-time with Trajectory Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we utilize the trip trajectories as inputs and propose a dual variational auto-encoder(VAE) framework, namely TripSafe, to estimate the probability of abnormal safety incidents. |
Yueyang Su; Di Yao; Xiaolei Zhou; Yuxuan Zhang; Yunxia Fan; Lu Bai; Jingping Bi; |
| 304 | TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While several SNE methods based on graph convolutional networks (GCN) have been proposed, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which measures the trustworthiness on edge signs for high-order relationships inferred by balance theory and corrects incorrect embedding propagation based on the trustworthiness. |
Min-Jeong Kim; Yeon-Chang Lee; Sang-Wook Kim; |
| 305 | UCTRL: Unbiased Contrastive Representation Learning Via Alignment and Uniformity for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Unbiased ConTrastive Representation Learning (uCTRL), optimizing alignment and uniformity functions derived from the InfoNCE loss function for CF models. |
Jae-woong Lee; Seongmin Park; Mincheol Yoon; Jongwuk Lee; |
| 306 | Unbiased Pairwise Learning from Implicit Feedback for Recommender Systems Without Biased Variance Control Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose an unbiased pairwise learning method, named UPL, with much lower variance to learn a truly unbiased recommender model. |
Yi Ren; Hongyan Tang; Jiangpeng Rong; Siwen Zhu; |
| 307 | Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we proposed a Uncertainty-aware Consistency learning framework for Cold-start item recommendation (shorten as UCC) solely based on user-item interactions. |
Taichi Liu; Chen Gao; Zhenyu Wang; Dong Li; Jianye Hao; Depeng Jin; Yong Li; |
| 308 | Uncertainty-based Heterogeneous Privileged Knowledge Distillation for Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While existing knowledge distillation methods have not taken these features into consideration, leading to suboptimal transfer weights. To overcome this limitation, we propose a novel algorithm called Uncertainty-based Heterogeneous Privileged Knowledge Distillation (UHPKD). |
Ang Li; Jian Hu; Ke Ding; Xiaolu Zhang; Jun Zhou; Yong He; Xu Min; |
| 309 | Unsupervised Dense Retrieval Training with Web Anchors Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present an unsupervised retrieval method with contrastive learning on web anchors. |
Yiqing Xie; Xiao Liu; Chenyan Xiong; |
| 310 | Unsupervised Dialogue Topic Segmentation with Topic-aware Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel unsupervised DTS framework, which learns topic-aware utterance representations from unlabeled dialogue data through neighboring utterance matching and pseudo-segmentation. |
Haoyu Gao; Rui Wang; Ting-En Lin; Yuchuan Wu; Min Yang; Fei Huang; Yongbin Li; |
| 311 | Unsupervised Query Performance Prediction for Neural Models with Pairwise Rank Preferences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While unsupervised approaches have been shown to work well for statistical IR models, it is likely that these approaches would yield limited effectiveness for neural ranking models (NRMs) because the retrieval scores of these models lie within a short range unlike their statistical counterparts. In this work, we propose to leverage a pairwise inference-based NRM’s (specifically, DuoT5) output to accumulate evidences on the pairwise believes of one document ranked above the other. |
Ashutosh Singh; Debasis Ganguly; Suchana Datta; Craig McDonald; |
| 312 | User-Dependent Learning to Debias for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a user-dependent IPS (UDIPS in short) method, which adaptively conducts propensity estimation for each user-item pair based on the user’s sensitivity to item popularity. |
Fangyuan Luo; Jun Wu; |
| 313 | Using Entropy for Group Sampling in Pairwise Ranking from Implicit Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the reliability of its user groups may be compromised as they only focus on a few behavioral similarities. To address this problem, this paper proposes a new entropy-weighted similarity measure for implicit feedback to quantify the relation between two users and sample like-minded user groups. |
Yujie Chen; Runlong Yu; Qi Liu; Enhong Chen; Zhenya Huang; |
| 314 | Weakly-Supervised Scientific Document Classification Via Retrieval-Augmented Multi-Stage Training Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In scientific domains, label names often include domain-specific concepts that may not appear in the document corpus, making it difficult to match labels and documents precisely. To tackle this issue, we propose WanDeR, which leverages dense retrieval to perform matching in the embedding space to capture the semantics of label names. |
Ran Xu; Yue Yu; Joyce Ho; Carl Yang; |
| 315 | When The Music Stops: Tip-of-the-Tongue Retrieval for Music Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately recall important identifying information. |
Samarth Bhargav; Anne Schuth; Claudia Hauff; |
| 316 | Where Does Your News Come From? Predicting Information Pathways in Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As social networks become further entrenched in modern society, it becomes increasingly important to understand and predict how information (e.g., news coverage of a given event) is propagated across social media (i.e., information pathway), which helps the understandings of the impact of real-world information. Thus, in this paper, we propose a novel task, Information Pathway Prediction (IPP), which depicts the propagation paths of a given passage as a community tree (rooted at the information source) on constructed community interaction graphs where we first aggregate individual users into communities formed around news sources and influential users, and then elucidate the patterns of information dissemination across media based on such community nodes. |
Alexander K. Taylor; Nuan Wen; Po-Nien Kung; Jiaao Chen; Violet Peng; Wei Wang; |
| 317 | Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. |
Chunjing Gan; Binbin Hu; Bo Huang; Tianyu Zhao; Yingru Lin; Wenliang Zhong; Zhiqiang Zhang; Jun Zhou; Chuan Shi; |
| 318 | WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To avoid thorough model re-training, we propose WSFE, a model-agnostic and training-free representation encoder, to be flexibly employed on the fly for effective user segmentation. |
Yankai Chen; Yifei Zhang; Menglin Yang; Zixing Song; Chen Ma; Irwin King; |
| 319 | EmoUS: Simulating User Emotions in Task-Oriented Dialogues Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we present EmoUS, a user simulator that learns to simulate user emotions alongside user behaviour. |
Hsien-Chin Lin; Shutong Feng; Christian Geishauser; Nurul Lubis; Carel van Niekerk; Michael Heck; Benjamin Ruppik; Renato Vukovic; Milica Gasić; |
| 320 | Repetition and Exploration in Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we examine whether repetition and exploration are important dimensions in the sequential recommendation scenario. |
Ming Li; Ali Vardasbi; Andrew Yates; Maarten de Rijke; |
| 321 | Balanced Topic Aware Sampling for Effective Dense Retriever: A Reproducibility Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we reproduce the balanced topic-aware sampling method; we do so for both the dataset used for evaluation in the original work (MS MARCO) and for a dataset in a different domain, that of product search (Amazon shopping queries dataset) to study whether the original results generalize to a different context. |
Shuai Wang; Guido Zuccon; |
| 322 | Reproducibility, Replicability, and Insights Into Dense Multi-Representation Retrieval Models: from ColBERT to Col* Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we inspect the reproducibility and replicability of the contextualised late interaction mechanism by extending ColBERT to Col⋆ which implements the late interaction mechanism across various pretrained models and different types of tokenisers. |
Xiao Wang; Craig Macdonald; Nicola Tonellotto; Iadh Ounis; |
| 323 | On Stance Detection in Image Retrieval for Argumentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct a comprehensive reproducibility study of the state of the art as represented by the CLEF’22 Touché lab and an in-house extension of it. |
Miriam Louise Carnot; Lorenz Heinemann; Jan Braker; Tobias Schreieder; Johannes Kiesel; Maik Fröbe; Martin Potthast; Benno Stein; |
| 324 | Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. |
Joakim Edin; Alexander Junge; Jakob D. Havtorn; Lasse Borgholt; Maria Maistro; Tuukka Ruotsalo; Lars Maaløe; |
| 325 | Query Performance Prediction: From Ad-hoc to Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Effective QPP could help a CS system to decide an appropriate action to be taken at the next turn. Despite its potential, QPP for CS has been little studied. We address this research gap by reproducing and studying the effectiveness of existing QPP methods in the context of CS. |
Chuan Meng; Negar Arabzadeh; Mohammad Aliannejadi; Maarten de Rijke; |
| 326 | An Empirical Comparison of Web Content Extraction Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Even less research has gone into the rigorous evaluation and a true apples-to-apples comparison of the few extraction systems that do exist. To get a better grasp on the current state of the art in the field, we combine and clean eight existing human-labeled web content extraction datasets. |
Janek Bevendorff; Sanket Gupta; Johannes Kiesel; Benno Stein; |
| 327 | Multimodal Neural Databases Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, methods in this field remain limited in the kinds of queries they support and, in particular, their inability to answer database-like queries. For this reason, inspired by recent work on neural databases, we propose a new framework, which we name Multimodal Neural Databases (MMNDBs). |
Giovanni Trappolini; Andrea Santilli; Emanuele Rodolà; Alon Halevy; Fabrizio Silvestri; |
| 328 | Take A Fresh Look at Recommender Systems from An Evaluation Standpoint Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, the commonly used train/test data splits and their consequences are re-examined. We begin by examining common data splitting methods, such as random split or leave-one-out, and discuss why the popularity baseline is poorly defined under such splits. |
Aixin Sun; |
| 329 | Where to Go Next for Recommender Systems? ID- Vs. Modality-based Recommender Models Revisited Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency. We aim to revisit this ‘old’ question and systematically study MoRec from several aspects. |
Zheng Yuan; Fajie Yuan; Yu Song; Youhua Li; Junchen Fu; Fei Yang; Yunzhu Pan; Yongxin Ni; |
| 330 | The Role of Relevance in Fair Ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we combine perspectives and tools from the social sciences, information retrieval, and fairness in machine learning to derive a set of desired criteria that relevance scores should satisfy in order to meaningfully guide fairness interventions. |
Aparna Balagopalan; Abigail Z. Jacobs; Asia J. Biega; |
| 331 | How Important Is Periodic Model Update in Recommender System? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: in this perspective paper, we formulate the delayed model update problem and quantitatively demonstrate the delayed model update actually harms the model performance by increasing the number of cold users and cold items increase and decreasing overall model performances. |
Hyunsung Lee; Sungwook Yoo; Dongjun Lee; Jaekwang Kim; |
| 332 | Metric-agnostic Ranking Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This leads to the following research question — how to optimize result ranking for complex ranking metrics without knowing their internal structures? To address this question, we conduct formal analysis on the limitation of existing ranking optimization techniques and describe three research tasks in Metric-agnostic Ranking Optimization: (1) develop surrogate metric models to simulate complex online ranking metrics on offline data; (2) develop differentiable ranking optimization frameworks for list or session level performance metrics without fine-grained supervision signals; and (3) develop efficient parameter exploration and exploitation techniques for ranking optimization in metric-agnostic scenarios. |
Qingyao Ai; Xuanhui Wang; Michael Bendersky; |
| 333 | T2Ranking: A Large-scale Chinese Benchmark for Passage Ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues. To address this problem, we introduce T2Ranking, a large-scale Chinese benchmark for passage ranking. |
Xiaohui Xie; Qian Dong; Bingning Wang; Feiyang Lv; Ting Yao; Weinan Gan; Zhijing Wu; Xiangsheng Li; Haitao Li; Yiqun Liu; Jin Ma; |
| 334 | BizGraphQA: A Dataset for Image-based Inference Over Graph-structured Diagrams from Business Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a collection of 10,000 synthetic graphs that faithfully reflect properties of real graphs in four business domains, and are realistically rendered within a PDF document with varying styles and layouts. |
Petr Babkin; William Watson; Zhiqiang Ma; Lucas Cecchi; Natraj Raman; Armineh Nourbakhsh; Sameena Shah; |
| 335 | Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions and Prospects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study aims to establish the foundation and motivate further pioneering research in the emerging field of VCRSs. |
Xinghua Qu; Hongyang Liu; Zhu Sun; Xiang Yin; Yew Soon Ong; Lu Lu; Zejun Ma; |
| 336 | SocialDial: A Benchmark for Socially-Aware Dialogue Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present the first socially-aware dialogue corpus — SocialDial based on Chinese social culture. |
Haolan Zhan; Zhuang Li; Yufei Wang; Linhao Luo; Tao Feng; Xiaoxi Kang; Yuncheng Hua; Lizhen Qu; Lay-Ki Soon; Suraj Sharma; Ingrid Zukerman; Zhaleh Semnani-Azad; Gholamreza Haffari; |
| 337 | U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED ) from real-world E-commerce scenarios. |
Yuanxing Liu; Weinan Zhang; Baohua Dong; Yan Fan; Hang Wang; Fan Feng; Yifan Chen; Ziyu Zhuang; Hengbin Cui; Yongbin Li; Wanxiang Che; |
| 338 | End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (e.g., support, refute or not enough information), and to generate a statement to summarize and explain the reasoning and ruling process. |
Barry Menglong Yao; Aditya Shah; Lichao Sun; Jin-Hee Cho; Lifu Huang; |
| 339 | Recipe-MPR: A Test Collection for Evaluating Multi-aspect Preference-based Natural Language Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We focus on the recipe domain in which multi-aspect preferences are often encountered due to the complexity of the human diet. |
Haochen Zhang; Anton Korikov; Parsa Farinneya; Mohammad Mahdi Abdollah Pour; Manasa Bharadwaj; Ali Pesaranghader; Xi Yu Huang; Yi Xin Lok; Zhaoqi Wang; Nathan Jones; Scott Sanner; |
| 340 | Beyond Single Items: Exploring User Preferences in Item Sets with The Conversational Playlist Curation Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: : this motivates conversational approaches-where users explicitly state or refine their preferences and systems elicit preferences in natural language-as an efficient way to understand user needs. We call this task conversational item set curation and present a novel data collection methodology that efficiently collects realistic preferences about item sets in a conversational setting by observing both item-level and set-level feedback. |
Arun Tejasvi Chaganty; Megan Leszczynski; Shu Zhang; Ravi Ganti; Krisztian Balog; Filip Radlinski; |
| 341 | Introducing MBIB – The First Media Bias Identification Benchmark Task and Dataset Collection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. |
Martin Wessel; Tomás Horych; Terry Ruas; Akiko Aizawa; Bela Gipp; Timo Spinde; |
| 342 | MG-ShopDial: A Multi-Goal Conversational Dataset for E-Commerce Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Yet, existing conversational datasets do not fully support the idea of mixing different conversational goals (i.e., search, recommendation, and question answering) and instead focus on a single goal. To address this, we introduce MG-ShopDial: a dataset of conversations mixing different goals in the domain of e-commerce. |
Nolwenn Bernard; Krisztian Balog; |
| 343 | Towards Explainable Conversational Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To better measure explainability in CRS, we propose ten evaluation perspectives based on the concepts from conventional recommender systems together with the characteristics of CRS. |
Shuyu Guo; Shuo Zhang; Weiwei Sun; Pengjie Ren; Zhumin Chen; Zhaochun Ren; |
| 344 | The JOKER Corpus: English-French Parallel Data for Multilingual Wordplay Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce and analyze a new dataset for research and applications in the retrieval and processing of wordplay. |
Liana Ermakova; Anne-Gwenn Bosser; Adam Jatowt; Tristan Miller; |
| 345 | Form-NLU: Dataset for The Form Natural Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a robust positional and logical relation-based form key-value information extraction framework. |
Yihao Ding; Siqu Long; Jiabin Huang; Kaixuan Ren; Xingxiang Luo; Hyunsuk Chung; Soyeon Caren Han; |
| 346 | MMEAD: MS MARCO Entity Annotations and Disambiguations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We specify a format to store and share links for both document and passage collections of MS MARCO. Following this specification, we release entity links to Wikipedia for documents and passages in both MS MARCO collections (v1 and v2). |
Chris Kamphuis; Aileen Lin; Siwen Yang; Jimmy Lin; Arjen P. de Vries; Faegheh Hasibi; |
| 347 | The Information Retrieval Experiment Platform Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We integrate irdatasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and even blinded retrieval experiments. |
Maik Fröbe; Jan Heinrich Reimer; Sean MacAvaney; Niklas Deckers; Simon Reich; Janek Bevendorff; Benno Stein; Matthias Hagen; Martin Potthast; |
| 348 | Towards A More User-Friendly and Easy-to-Use Benchmark Library for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a significant update of RecBole, making it more user-friendly and easy-to-use as a comprehensive benchmark library for recommendation. |
Lanling Xu; Zhen Tian; Gaowei Zhang; Junjie Zhang; Lei Wang; Bowen Zheng; Yifan Li; Jiakai Tang; Zeyu Zhang; Yupeng Hou; Xingyu Pan; Wayne Xin Zhao; Xu Chen; Ji-Rong Wen; |
| 349 | The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The AQL is the first to do so, enabling research on new retrieval models and (diachronic) search engine analyses. |
Jan Heinrich Reimer; Sebastian Schmidt; Maik Fröbe; Lukas Gienapp; Harrisen Scells; Benno Stein; Matthias Hagen; Martin Potthast; |
| 350 | GammaGL: A Multi-Backend Library for Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To provide a more convenient user experience, we present Gamma Graph Library (GammaGL), a GNN library that supports multiple deep learning frameworks as backends. |
Yaoqi Liu; Cheng Yang; Tianyu Zhao; Hui Han; Siyuan Zhang; Jing Wu; Guangyu Zhou; Hai Huang; Hui Wang; Chuan Shi; |
| 351 | Tieval: An Evaluation Framework for Temporal Information Extraction Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To mitigate these problems, we have developed tieval, a Python library that provides a concise interface for importing different corpora and is equipped with domain-specific operations that facilitate system evaluation. In this paper, we present the first public release of tieval and highlight its most relevant features. |
Hugo Sousa; Ricardo Campos; Alípio Mário Jorge; |
| 352 | HC3: A Suite of Test Collections for CLIR Evaluation Over Informal Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a new pair of CLIR test collections with millions of Chinese or Persian Tweets or Tweet threads as documents, sixty event-motivated topics written both in English and in each of the two document languages, and three-point graded relevance judgments constructed using interactive search and active learning. |
Dawn Lawrie; James Mayfield; Douglas W. Oard; Eugene Yang; Suraj Nair; Petra Galuščáková; |
| 353 | RecStudio: Towards A Highly-Modularized Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we develop a highly-modularized recommender system — RecStudio, in which any recommendation algorithm is categorized into either a ranker or a retriever. |
Defu Lian; Xu Huang; Xiaolong Chen; Jin Chen; Xingmei Wang; Yankai Wang; Haoran Jin; Rui Fan; Zheng Liu; Le Wu; Enhong Chen; |
| 354 | MR2: A Benchmark for Multimodal Retrieval-Augmented Rumor Detection in Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing datasets for rumor detection mainly focus on a single modality i.e., text. To bridge this gap, we construct MR2, a multimodal multilingual retrieval-augmented dataset for rumor detection. |
Xuming Hu; Zhijiang Guo; Junzhe Chen; Lijie Wen; Philip S. Yu; |
| 355 | BioSift: A Dataset for Filtering Biomedical Abstracts for Drug Repurposing and Clinical Meta-Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a new, original document classification dataset, BioSift, to expedite the initial selection and labeling of studies for drug repurposing. |
David Kartchner; Irfan Al-Hussaini; Haydn Turner; Jennifer Deng; Shubham Lohiya; Prasanth Bathala; Cassie Mitchell; |
| 356 | MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present MoocRadar, a fine-grained, multi-aspect knowledge repository consisting of 2,513 exercise questions, 5,600 knowledge concepts, and over 12 million behavioral records. |
Jifan Yu; Mengying Lu; Qingyang Zhong; Zijun Yao; Shangqing Tu; Zhengshan Liao; Xiaoya Li; Manli Li; Lei Hou; Hai-Tao Zheng; Juanzi Li; Jie Tang; |
| 357 | RL4RS: A Real-World Dataset for Reinforcement Learning Based Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce the first open-source real-world dataset, RL4RS, hoping to replace the artificial datasets and semi-simulated RS datasets previous studies used due to the resource limitation of the RL-based RS domain. |
Kai Wang; Zhene Zou; Minghao Zhao; Qilin Deng; Yue Shang; Yile Liang; Runze Wu; Xudong Shen; Tangjie Lyu; Changjie Fan; |
| 358 | JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We argue that evaluating with such a dataset may yield unreliable results and conclusions, and deviate from real user satisfaction. To overcome these problems, in this paper, we release a personalized product search dataset comprised of real user queries and diverse user-product interaction types (clicking, adding to cart, following, and purchasing) collected from JD.com, a popular Chinese online shopping platform. |
Jiongnan Liu; Zhicheng Dou; Guoyu Tang; Sulong Xu; |
| 359 | IQPP: A Benchmark for Image Query Performance Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To boost the exploration of the QPP task in image retrieval, we propose the first benchmark for image query performance prediction (iQPP). |
Eduard Poesina; Radu Tudor Ionescu; Josiane Mothe; |
| 360 | SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we provide SPRINT, a unified python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval. |
Nandan Thakur; Kexin Wang; Iryna Gurevych; Jimmy Lin; |
| 361 | AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While vision–language pretrained transformers have led to significant improvements in retrieval effectiveness, existing research has relied on image-caption datasets that feature only simplistic image–text relationships and underspecified user models of retrieval tasks. To address the gap between these oversimplified settings and real-world applications for multimedia content creation, we introduce a new approach for building retrieval test collections. |
Jheng-Hong Yang; Carlos Lassance; Rafael Sampaio De Rezende; Krishna Srinivasan; Miriam Redi; Stéphane Clinchant; Jimmy Lin; |
| 362 | DICE: A Dataset of Italian Crime Event News Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The contribution of the paper are: (1) the creation of a corpus of 10,395 crime news; (2) the annotation schema; (3) a dataset of 10,395 news with automatic annotations; (4) a preliminary manual annotation using the proposed schema of 1000 documents. |
Giovanni Bonisoli; Maria Pia Di Buono; Laura Po; Federica Rollo; |
| 363 | BDI-Sen: A Sentence Dataset for Clinical Symptoms of Depression Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce BDI-Sen, a symptom-annotated sentence dataset for depressive disorder. |
Anxo Pérez; Javier Parapar; Álvaro Barreiro; Silvia Lopez-Larrosa; |
| 364 | MobileRec: A Large Scale Dataset for Mobile Apps Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To facilitate research for app recommendation systems, we introduce a large-scale dataset, called MobileRec. |
M. H. Maqbool; Umar Farooq; Adib Mosharrof; A. B. Siddique; Hassan Foroosh; |
| 365 | MythQA: Query-Based Large-Scale Check-Worthy Claim Detection Through Multi-Answer Open-Domain Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Many efforts have been put into how to identify check-worthy claims from a small scale of pre-collected claims, but how to efficiently detect check-worthy claims directly from a large-scale information source, such as Twitter, remains underexplored. To fill this gap, we introduce MythQA, a new multi-answer open-domain question answering(QA) task that involves contradictory stance mining for query-based large-scale check-worthy claim detection. |
Yang Bai; Anthony Colas; Daisy Zhe Wang; |
| 366 | RiverText: A Python Library for Training and Evaluating Incremental Word Embeddings from Text Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, despite their widespread use, traditional word embedding models present a limitation in their static nature, which hampers their ability to adapt to the constantly evolving language patterns that emerge in sources such as social media and the web (e.g., new hashtags or brand names). To overcome this problem, incremental word embedding algorithms are introduced, capable of dynamically updating word representations in response to new language patterns and processing continuous data streams. |
Gabriel Iturra-Bocaz; Felipe Bravo-Marquez; |
| 367 | FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms. |
Penghui Wei; Hongjian Dou; Shaoguo Liu; Rongjun Tang; Li Liu; Liang Wang; Bo Zheng; |
| 368 | The BETTER Cross-Language Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These datasets are freely available to researchers working in cross-language retrieval, information extraction, or the conjunction of IR and IE. This paper describes the datasets, how they were constructed, and how they might be used by researchers. |
Ian Soboroff; |
| 369 | REFinD: Relation Extraction Financial Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these datasets fail to capture financial-domain specific challenges since most of these datasets are compiled using general knowledge sources such as Wikipedia, web-based text and news articles, hindering real-life progress and adoption within the financial world. To address this limitation, we propose REFinD, the first large-scale annotated dataset of relations, with ~29K instances and 22 relations amongst 8 types of entity pairs, generated entirely over financial documents. |
Simerjot Kaur; Charese Smiley; Akshat Gupta; Joy Sain; Dongsheng Wang; Suchetha Siddagangappa; Toyin Aguda; Sameena Shah; |
| 370 | Linked-DocRED – Enhancing DocRED with Entity-Linking to Evaluate End-To-End Document-Level Information Extraction Pipelines Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, we propose Linked-DocRED, to the best of our knowledge, the first manually-annotated, large-scale, document-level IE dataset. We enhance the existing and widely-used DocRED dataset with entity-linking labels that are generated thanks to a semi-automatic process that guarantees high-quality annotations. |
Pierre-Yves Genest; Pierre-Edouard Portier; Elöd Egyed-Zsigmond; Martino Lovisetto; |
| 371 | DECAF: A Modular and Extensible Conversational Search Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This exacerbates the reproducibility crisis already observed in several research areas, including Information Retrieval (IR). To address this issue, we propose DECAF: a modular and extensible conversational search framework designed for fast prototyping and development of conversational agents. |
Marco Alessio; Guglielmo Faggioli; Nicola Ferro; |
| 372 | LongEval-Retrieval: French-English Dynamic Test Collection for Continuous Web Search Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To do that, we introduce the concept of a dynamic test collection that is composed of successive sub-collections each representing the state of an information system at a given time step. |
Petra Galuscáková; Romain Deveaud; Gabriela González Sáez; Philippe Mulhem; Lorraine Goeuriot; Florina Piroi; Martin Popel; |
| 373 | LibVQ: A Toolkit for Optimizing Vector Quantization and Efficient Neural Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, there have also been multiple algorithms which make the retriever and VQ better collaborated to alleviate such a loss. On top of these progresses, we develop LibVQ, which optimizes vector quantization for efficient dense retrieval. |
Chaofan Li; Zheng Liu; Shitao Xiao; Yingxia Shao; Defu Lian; Zhao Cao; |
| 374 | A Preference Judgment Tool for Authoritative Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we present a new preference judgment tool called JUDGO, designed for expert assessors and researchers. |
Mahsa Seifikar; Linh Nhi Phan Minh; Negar Arabzadeh; Charles L. A. Clarke; Mark D. Smucker; |
| 375 | VoMBaT: A Tool for Visualising Evaluation Measure Behaviour in High-Recall Search Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a new visual analytics tool to explore the dynamics of evaluation measures depending on recall level. |
Wojciech Kusa; Aldo Lipani; Petr Knoth; Allan Hanbury; |
| 376 | Searching The ACL Anthology with Math Formulas and Text Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The new MathDeck system searches PDF documents in a portion of the ACL Anthology using both formulas and text, and shows matched words and formulas along with other extracted formulas in-context. |
Bryan Amador; Matt Langsenkamp; Abhisek Dey; Ayush Kumar Shah; Richard Zanibbi; |
| 377 | One Stop Shop for Question-Answering Dataset Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we offer a new visualization tool — Dataset Statistical View (DSV), to lower the barrier of research entry by providing easy access to the question-answering (QA) datasets that researchers can build their work upon. |
Chang Nian Chuy; Qinmin Vivian Hu; Chen Ding; |
| 378 | Tevatron: An Efficient and Flexible Toolkit for Neural Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Tevatron, a neural retrieval toolkit that is optimized for efficiency, flexibility, and code simplicity. |
Luyu Gao; Xueguang Ma; Jimmy Lin; Jamie Callan; |
| 379 | Profiling and Visualizing Dynamic Pruning Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a diagnostic framework, Dyno, for profiling and visualizing the performance of dynamic pruning algorithms. |
Zhixuan Li; Joel Mackenzie; |
| 380 | MetroScope: An Advanced System for Real-Time Detection and Analysis of Metro-Related Threats and Events Via Twitter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We addressed those issues by developing the MetroScope system, a real-time threat/event detection system applied to Washington D.C. metro system. |
Jianfeng He; Syuan-Ying Wu; Abdulaziz Alhamadani; Chih-Fang Chen; Wen-Fang Lu; Chang-Tien Lu; David Solnick; Yanlin Li; |
| 381 | SciHarvester: Searching Scientific Documents for Numerical Values Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A challenge for search technologies is to support scientific literature surveys that present overviews of the reported numerical values documented for specific physical properties. We present SciHarvester, a system tailored to address this problem for agronomic science. |
Maciej Rybinski; Stephen Wan; Sarvnaz Karimi; Cecile Paris; Brian Jin; Neil Huth; Peter Thorburn; Dean Holzworth; |
| 382 | NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: NeuralKG-ind is the first library of inductive KGRL as an important update of NeuralKG library. |
Wen Zhang; Zhen Yao; Mingyang Chen; Zhiwei Huang; Huajun Chen; |
| 383 | AMICA: Alleviating Misinformation for Chinese Americans Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present AMICA, an information retrieval system for alleviating misinformation for Chinese Americans. |
Xiaoxiao Shang; Ye Chen; Yi Fang; Yuhong Liu; Subramaniam Vincent; |
| 384 | PEPO: Petition Executing Processing Optimizer Based on Natural Language Processing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose "Petition Executing Process Optimizer (PEPO)," an AI-based petition processing system that features three components, (a) Department Classification, (b) Importance Assessment, and (c) Response Generation for improving the Public Work Bureau (PWB) 1999 Hotline petitions handling process in Taiwan. |
Yin-Wei Chiu; Hsiao-Ching Huang; Cheng-Ju Lee; Hsun-Ping Hsieh; |
| 385 | HeteroCS: A Heterogeneous Community Search System With Semantic Explanation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we revisit the community search problem on heterogeneous networks and introduce a novel paradigm of heterogeneous community search and ranking. |
Weibin Cai; Fanwei Zhu; Zemin Liu; Minghui Wu; |
| 386 | OpenMatch-v2: An All-in-one Multi-Modality PLM-based Information Retrieval Toolkit Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a Python-based IR toolkit OpenMatch-v2. |
Shi Yu; Zhenghao Liu; Chenyan Xiong; Zhiyuan Liu; |
| 387 | FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, each GNN-based user profiling approach has its own way of processing information, thus creating heterogeneity that does not favour the benchmarking of these techniques. To overcome this issue, we present FairUP, a framework that standardises the input needed to run three state-of-the-art GNN-based models for user profiling tasks. |
Mohamed Abdelrazek; Erasmo Purificato; Ludovico Boratto; Ernesto William De Luca; |
| 388 | Tahaqqaq: A Real-Time System for Assisting Twitter Users in Arabic Claim Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, systems that operate over Arabic content are scarce. In this work, we bridge this gap by proposing Tahaqqaq (Verify), an Arabic real-time system that helps users verify claims over Twitter with several functionalities, such as identifying check-worthy claims, estimating credibility of users in terms of spreading fake news, and finding authoritative accounts. |
Zien Sheikh Ali; Watheq Mansour; Fatima Haouari; Maram Hasanain; Tamer Elsayed; Abdulaziz Al-Ali; |
| 389 | SEA: A Scalable Entity Alignment System Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. |
Junyang Wu; Tianyi Li; Lu Chen; Yunjun Gao; Ziheng Wei; |
| 390 | A Retrieval System for Images and Videos Based on Aesthetic Assessment of Visuals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present our tool that can assess image and video content from an aesthetic standpoint. |
Daniel Vera Nieto; Saikishore Kalloori; Fabio Zund; Clara Fernandez Labrador; Marc Willhaus; Severin Klingler; Markus Gross; |
| 391 | XpmIR: A Modular Library for Learning to Rank and Neural IR Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present XpmIR, a Python library defining a reusable set of experimental components. |
Yuxuan Zong; Benjamin Piwowarski; |
| 392 | Pybool_ir: A Toolkit for Domain-Specific Search Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, off-the-shelf tools have their own nuanced query languages and do not allow directly using the often large and complicated Boolean queries seen in domain-specific search scenarios. The pybool_ir toolkit aims to address these problems and to lower the barrier to entry for developing new methods for domain-specific search. |
Harrisen Scells; Martin Potthast; |
| 393 | TIB AV-Analytics: A Web-based Platform for Scholarly Video Analysis and Film Studies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a novel web-based video analysis platform called TIB AV-Analytics (TIB-AV-A). |
Matthias Springstein; Markos Stamatakis; Margret Plank; Julian Sittel; Roman Mauer; Oksana Bulgakowa; Ralph Ewerth; Eric Müller-Budack; |
| 394 | SONAR: Web-based Tool for Multimodal Exploration of Non-Fungible Token Inspiration Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present SONAR, a web-based tool for multimodal exploration of Non-Fungible Token (NFT) inspiration networks. |
Lucio La Cava; Davide Costa; Andrea Tagarelli; |
| 395 | Searching for Reliable Facts Over A Medical Knowledge Base Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents CoreKB, a Web platform for searching reliable facts over gene expression-cancer associations Knowledge Base (KB). |
Fabio Giachelle; Stefano Marchesin; Gianmaria Silvello; Omar Alonso; |
| 396 | Ranxhub: An Online Repository for Information Retrieval Runs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we provide a platform for sharing pre-computed runs: the ranked lists of documents retrieved for a specific set of queries by a retrieval model. |
Elias Bassani; |
| 397 | Podify: A Podcast Streaming Platform with Automatic Logging of User Behaviour for Academic Research Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, there is a need for a podcast streaming platform that reduces the overhead of conducting user studies. To address these issues, in this work, we present Podify. |
Francesco Meggetto; Yashar Moshfeghi; |
| 398 | Exploratory Visualization Tool for The Continuous Evaluation of Information Retrieval Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel visualization tool that facilitates the exploratory analysis of continuous evaluation for information retrieval systems. |
Gabriela González-Sáez; Petra Galuscáková; Romain Deveaud; Lorraine Goeuriot; Philippe Mulhem; |
| 399 | Multi-lingual Semantic Search for Domain-specific Applications: Adobe Photoshop and Illustrator Help Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we leverage sentence-BERT models fine-tuned on Adobe’s HelpX data to perform multi-lingual semantic search on help and tutorial documents. |
Jayant Kumar; Ashok Gupta; Zhaoyu Lu; Andrei Stefan; Tracy Holloway King; |
| 400 | Bootstrapping Query Suggestions in Spotify’s Instant Search System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explain how we introduce query suggestions in Spotify’s instant search system–a system that connects hundreds of millions of users with billions of items in our audio catalog. |
Alva Liu; Humberto Jesús Corona Pampin; Enrico Palumbo; |
| 401 | COUPA: An Industrial Recommender System for Online to Offline Service Platforms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Aiming at helping users locally discover retail services (e.g., entertainment and dining) on Online to Offline (O2O) service platforms, we propose COUPA, an industrial system targeting for characterizing user preference with inspiring considerations of time and position aware preferences. |
Sicong Xie; Binbin Hu; Fengze Li; Ziqi Liu; Zhiqiang Zhang; Wenliang Zhong; Jun Zhou; |
| 402 | A Consumer Compensation System in Ride-hailing Service Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a consumer compensation system, where a transfer learning enhanced uplift modeling is designed to measure the elasticity, and a model predictive control based optimization is formulated to control the budget accurately. |
Zhe Yu; Chi Xia; Shaosheng Cao; Lin Zhou; Haibin Huang; |
| 403 | Interactive Recommendation System for Meituan Waimai Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we find that it will undermine use fluency and increase use complexity by rashly inserting a new question UI when users browse the homepage. Therefore, we develop an Embedded Interactive Recommender System (EIRS) that directly infers users’ intention according to their click behaviors on the homepage and dynamically inserts a new recommendation result into the homepage1. |
Chen Ji; Yacheng Li; Rui Li; Fei Jiang; Xiang Li; Wei Lin; Chenglong Zhang; Wei Wang; Shuyang Wang; |
| 404 | Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conduct an analysis of embedding-based retrieval launched in early 2021 on our social network search engine, and define two main categories of failures introduced by it, integrity and junkiness. |
Wenping Wang; Yunxi Guo; Chiyao Shen; Shuai Ding; Guangdeng Liao; Hao Fu; Pramodh Karanth Prabhakar; |
| 405 | Dialog-to-Actions: Building Task-Oriented Dialogue System Via Action-Level Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a task-oriented dialogue system via action-level generation. |
Yuncheng Hua; Xiangyu Xi; Zheng Jiang; Guanwei Zhang; Chaobo Sun; Guanglu Wan; Wei Ye; |
| 406 | Long-Form Information Retrieval for Enterprise Matchmaking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address the long-form information retrieval challenges by proposing a combination of (i) traditional retrieval methods, to leverage the lexical match from the query, and (ii) state-of-the-art sentence transformers, to capture the rich context in the long queries. |
Pengyuan Li; Guang-Jie Ren; Anna Lisa Gentile; Chad DeLuca; Daniel Tan; Sandeep Gopisetty; |
| 407 | Learning Query-aware Embedding Index for Improving E-commerce Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel query-aware embedding Index framework, which aligns the query and item embedding space to reduce the distance between positive pairs, thereby mixing the query and item embeddings to learn better cluster centers for product quantization. |
Mingming Li; Chunyuan Yuan; Binbin Wang; Jingwei Zhuo; Songlin Wang; Lin Liu; Sulong Xu; |
| 408 | A Practical Online Allocation Framework at Industry-scale in Constrained Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While the existing literature has made significant progress in improving recommendation algorithms for various scenarios, less attention has been given to developing and deploying industry-scale online allocation system in an efficient manner. To address this issue, this paper introduces an integrated and efficient learning framework in constrained recommendation scenarios at Alipay. |
Daohong Jian; Yang Bao; Jun Zhou; Hua Wu; |
| 409 | TMML: Text-Guided MuliModal Product Location For Alleviating Retrieval Inconsistency in E-Commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We can easily determine which product is on sale through the hint of the title, so we propose Text-Guided MuliModal Product Location (TMML) to use additional product titles to assist in locating the actual selling product instance. |
Youhua Tang; Xiong Xiong; Siyang Sun; Baoliang Cui; Yun Zheng; Haihong Tang; |
| 410 | MDI: A Debiasing Method Combining Unbiased and Biased Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel data imputation approach that combines an unbiased model and a debiasing model with adaptively learnt weights. |
Han Zhao; Qing Cui; Xinyu Li; Rongzhou Bao; Longfei Li; Jun Zhou; Zhehao Liu; Jinghua Feng; |
| 411 | Context-Aware Classification of Legal Document Pages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although in recent years a few techniques have been proposed to exploit the context information from neighboring pages to enhance document page classification, they typically cannot be utilized with large pre-trained language models due to the constraint on input length. In this paper, we present a simple but effective approach that overcomes the above limitation. |
Pavlos Fragkogiannis; Martina Forster; Grace E. Lee; Dell Zhang; |
| 412 | Facebook Content Search: Efficient and Effective Adapting Search on A Large Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we discuss the challenges of Facebook content search in depth, and then describe our novel approach to efficiently handling a massive number of documents with advanced query understanding, retrieval, and machine learning techniques. |
Xiangyu Niu; Yu-Wei Wu; Xiao Lu; Gautam Nagpal; Philip Pronin; Kecheng Hao; Zhen Liao; Guangdeng Liao; |
| 413 | Practice and Challenges in Building A Business-oriented Search Engine Quality Metric Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a model-based quality metric using Explainable Boosting Machine as the classifier and online user behaviour signals as features to predict search quality. |
Nuo Chen; Donghyun Park; Hyungae Park; Kijun Choi; Tetsuya Sakai; Jinyoung Kim; |
| 414 | Building A Graph-Based Patent Search Engine Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a graph-based patent search engine that tries to mimic the work done by a professional patent examiner. |
Sebastian Björkqvist; Juho Kallio; |
| 415 | A Data-centric Solution to Improve Online Performance of Customer Service Bots Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fix badcases and improve online performance of chatbots in a timely and continuous manner, we propose a data-centric solution consisting of three main modules: badcase detection, bad case correction, and answer extraction. |
Sen Hu; Changlin Yang; Junjie Wang; Siye Liu; Teng Xu; Wangshu Zhang; Jing Zheng; |
| 416 | Enhancing Dynamic Image Advertising with Vision-Language Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a vision-language framework for query-image matching. |
Zhoufutu Wen; Xinyu Zhao; Zhipeng Jin; Yi Yang; Wei Jia; Xiaodong Chen; Shuanglong Li; Lin Liu; |
| 417 | Graph Enhanced BERT for Query Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, search logs contain user clicks between queries and urls that provide rich users’ search behavioral information on queries beyond their content. Therefore, in this paper, we aim to fill this gap by exploring search logs. |
Juanhui Li; Wei Zeng; Suqi Cheng; Yao Ma; Jiliang Tang; Shuaiqiang Wang; Dawei Yin; |
| 418 | KATIE: A System for Key Attributes Identification in Product Knowledge Graph Construction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present part of Huawei’s efforts in building a Product Knowledge Graph (PKG). |
Btissam Er-Rahmadi; Arturo Oncevay; Yuanyi Ji; Jeff Z. Pan; |
| 419 | A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we adapt substitute recommendations into language matching problem. |
Wenting Ye; Hongfei Yang; Shuai Zhao; Haoyang Fang; Xingjian Shi; Naveen Neppalli; |
| 420 | Embedding Based Retrieval in Friend Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our contributions in this work include deploying a novel retrieval system to a large-scale friend recommendation system at Snapchat, generating embeddings for billions of users using Graph Neural Networks, and building EBR infrastructure in production to support Snapchat scale. |
Jiahui Shi; Vivek Chaurasiya; Yozen Liu; Shubham Vij; Yan Wu; Satya Kanduri; Neil Shah; Peicheng Yu; Nik Srivastava; Lei Shi; Ganesh Venkataraman; Jun Yu; |
| 421 | Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We discuss open problems and challenges with respect to modeling spoken information queries for virtual assistants, and list opportunities where Information Retrieval methods and research can be applied to improve the quality of virtual assistant speech recognition. |
Christophe Van Gysel; |
| 422 | Personalized Stock Recommendation with Investors’ Attention and Contextual Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Personalized Stock Recommendation with Investors’ Attention and Contextual Information (PSRIC). |
Takehiro Takayanagi; Kiyoshi Izumi; Atsuo Kato; Naoyuki Tsunedomi; Yukina Abe; |
| 423 | Synerise Monad: A Foundation Model for Behavioral Event Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new aspect of Monad: private foundation models for behavioral data, trained on top of UBRs. |
Barbara Rychalska; Szymon Lukasik; Jacek Dabrowski; |
| 424 | Extracting Complex Named Entities in Legal Documents Via Weakly Supervised Object Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel system that combines object detection for Document Layout Analysis (DLA) with weakly supervised learning to address the challenge of extracting discontinuous complex named entities in legal documents. |
Hsiu-Wei Yang; Abhinav Agrawal; |
| 425 | Exploring The Spatiotemporal Features of Online Food Recommendation Service Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: There have been a variety of studies that have begun to explore its spatiotemporal properties, but a comprehensive and in-depth analysis of the OFRS spatiotemporal features is yet to be conducted. Therefore, this paper studies the OFRS based on three questions: how spatiotemporal features play a role; why self-attention cannot be used to model the spatiotemporal sequences of OFRS; and how to combine spatiotemporal features to improve the efficiency of OFRS. |
Shaochuan Lin; Jiayan Pei; Taotao Zhou; Hengxu He; Jia Jia; Ning Hu; |
| 426 | Alleviating Matching Bias in Marketing Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore how to alleviate the matching bias from the causal-effect perspective. |
Junpeng Fang; Qing Cui; Gongduo Zhang; Caizhi Tang; Lihong Gu; Longfei Li; Jinjie Gu; Jun Zhou; Fei Wu; |
| 427 | GreenSeq: Automatic Design of Green Networks for Sequential Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, developing lightweight yet effective SR models has become a frequent demand in industrial applications, which is also in line with the ideals of Green AI and Green IR. In this applied paper, we introduce GreenSeq deployed in Alipay to automatically design Green networks that can provide appropriate recommendations with lower computational consumption in SR. |
Yankun Ren; Xinxing Yang; Xingyu Lu; Longfei Li; Jun Zhou; Jinjie Gu; Guannan Zhang; |
| 428 | DCBT: A Simple But Effective Way for Unified Warm and Cold Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, none of them pay attention to the discrepancy between model predictions and true likelihoods of cold items, while over- or under-estimation is harmful to the ROI (Return on Investment) of advertising placements. In this paper, in order to address the above issues, we propose a framework called Distribution-Constrained Batch Transformer (DCBT). |
Jieyu Yang; Liang Zhang; Yong He; Ke Ding; Zhaoxin Huan; Xiaolu Zhang; Linjian Mo; |
| 429 | Building K-Anonymous User Cohorts with Consecutive Consistent Weighted Sampling (CCWS) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a scalable K-anonymous cohort building algorithm called consecutive consistent weighted sampling (CCWS). |
Xinyi Zheng; Weijie Zhao; Xiaoyun Li; Ping Li; |
| 430 | Implicit Query Parsing at Amazon Product Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we demonstrate the critical importance of implicit attributes in real-world product search engines. |
Chen Luo; Rahul Goutam; Haiyang Zhang; Chao Zhang; Yangqiu Song; Bing Yin; |
| 431 | Delving Into E-Commerce Product Retrieval with Vision-Language Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel V+L pre-training method to solve the retrieval problem in Taobao Search. |
Xiaoyang Zheng; Fuyu Lv; Zilong Wang; Qingwen Liu; Xiaoyi Zeng; |
| 432 | Improving Programming Q&A with Neural Generative Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Its application boosts developer productivity by aiding developers in quickly finding programming answers from the vast amount of information on the Internet. In this study, we propose ProQANS and its variants ReProQANS and ReAugProQANS to tackle programming Q&A. |
Suthee Chaidaroon; Xiao Zhang; Shruti Subramaniyam; Jeffrey Svajlenko; Tanya Shourya; Iman Keivanloo; Ria Joy; |
| 433 | Contextual Multilingual Spellchecker for User Queries Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Finally, since most enterprises have unique vocabularies such as product names, off-the-shelf spelling solutions fall short of users’ needs. In this work, we build a multilingual spellchecker that is extremely fast and scalable and that adapts its vocabulary and hence speller output based on a specific product’s needs. |
Sanat Sharma; Josep Valls-Vargas; Tracy Holloway King; Francois Guerin; Chirag Arora; |
| 434 | Exploring 360-Degree View of Customers for Lookalike Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a novel framework that unifies the customers’ different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. |
Md Mostafizur Rahman; Daisuke Kikuta; Satyen Abrol; Yu Hirate; Toyotaro Suzumura; Pablo Loyola; Takuma Ebisu; Manoj Kondapaka; |
| 435 | Semantic-enhanced Modality-asymmetric Retrieval for Online E-commerce Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the multimodal retrieval problem, where the visual information (e.g, image) of item is leveraged as supplementary of textual information to enrich item representation and further improve retrieval performance. |
Zhigong Zhou; Ning Ding; Xiaochuan Fan; Yue Shang; Yiming Qiu; Jingwei Zhuo; Zhiwei Ge; Songlin Wang; Lin Liu; Sulong Xu; Han Zhang; |
| 436 | OFAR: A Multimodal Evidence Retrieval Framework for Illegal Live-streaming Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, the professional needs to search for related evidence from a large-scale knowledge database for evaluating whether a given live-streaming clip contains illegal behavior, which is time-consuming and laborious. To address this issue, in this work, we propose a multimodal evidence retrieval system, named OFAR, to facilitate the illegal live-streaming identification. |
Dengtian Lin; Yang Ma; Yuhong Li; Xuemeng Song; Jianlong Wu; Liqiang Nie; |
| 437 | How Well Do Offline Metrics Predict Online Performance of Product Ranking Models? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To use offline metrics for effective model selection, a major challenge is to understand how well offline metrics predict which ranking models perform better in online experiments. This paper aims to address this challenge in product search ranking. |
Xiaojie Wang; Ruoyuan Gao; Anoop Jain; Graham Edge; Sachin Ahuja; |
| 438 | AttriBERT – Session-based Product Attribute Recommendation with BERT Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a session-based recommendation system (SBRS) which recommends refinements by inferring product attribute preferences of customers based on the sequence of products viewed earlier in the session. |
Akshay Jagatap; Nikki Gupta; Sachin Farfade; Prakash Mandayam Comar; |