Paper Digest: SIGIR 2024 Papers & Highlights
SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval) is one of the top information retrieval conferences in the world. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
To search or review papers within SIGIR-2024 related to a specific topic, please use the search by venue (SIGIR-2024), review by venue (SIGIR-2024) and question answering by venue (SIGIR-2024) services. To browse papers by author, here is a list of all 1,500 authors (SIGIR-2024). You may also like to explore our “Best Paper” Digest (SIGIR), which lists the most influential SIGIR papers since 1981.
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TABLE 1: Paper Digest: SIGIR 2024 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Nevertheless, these methods face issues like plausible examples retrieved without task-specific state transition dynamics and long input with plenty of irrelevant context due to using complete trajectories. In this paper, we propose a novel framework (TRAD) to tackle these problems. |
Ruiwen Zhou; Yingxuan Yang; Muning Wen; Ying Wen; Wenhao Wang; Chunling Xi; Guoqiang Xu; Yong Yu; Weinan Zhang; |
| 2 | In-Context Learning Or: How I Learned to Stop Worrying and Love Applied Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While standard unsupervised ranking models can be used to retrieve these few-shot examples from a training set, the effectiveness of the examples can potentially be improved by re-defining the notion of relevance specific to its utility for the downstream task, i.e., considering an example to be relevant if including it in the prompt instruction leads to a correct prediction. With this task-specific notion of relevance, it is possible to train a supervised ranking model (e.g., a bi-encoder or cross-encoder), which potentially learns to optimally select the few-shot examples. |
Andrew Parry; Debasis Ganguly; Manish Chandra; |
| 3 | CorpusLM: Towards A Unified Language Model on Corpus for Knowledge-Intensive Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose CorpusLM, a unified language model that leverages external corpus to tackle various knowledge-intensive tasks by integrating generative retrieval, closed-book generation, and RAG through a unified greedy decoding process. |
Xiaoxi Li; Zhicheng Dou; Yujia Zhou; Fangchao Liu; |
| 4 | A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. |
Shengyao Zhuang; Honglei Zhuang; Bevan Koopman; Guido Zuccon; |
| 5 | Unsupervised Large Language Model Alignment for Information Retrieval Via Contrastive Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This limits the performance of LLMs in IR because finding and distinguishing relevant documents from substantial similar documents is a typical problem in many IR tasks. To address this issue, we propose an unsupervised alignment method, namely Reinforcement Learning from Contrastive Feedback (RLCF), empowering LLMs to generate both high-quality and context-specific responses. |
Qian Dong; Yiding Liu; Qingyao Ai; Zhijing Wu; Haitao Li; Yiqun Liu; Shuaiqiang Wang; Dawei Yin; Shaoping Ma; |
| 6 | MetaHKG: Meta Hyperbolic Learning for Few-shot Temporal Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hyperbolic space is advantageous for modeling emerging graph entities for two reasons: First, its geometric property of exponential expansion aligns with the rapid growth of new entities in real-world graphs; Second, it excels in capturing power-law patterns and hierarchical structures, well-suitable for new entities distributed at the peripheries of graph hierarchies and loosely connected with others through few links. We therefore propose a meta-learning framework, MetaHKG, to enable few-shot temporal reasoning within a hyperbolic space. |
Ruijie Wang; Yutong Zhang; Jinyang Li; Shengzhong Liu; Dachun Sun; Tianchen Wang; Tianshi Wang; Yizhuo Chen; Denizhan Kara; Tarek Abdelzaher; |
| 7 | Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This is mainly because of (1) insufficiently exploring the internal structure and semantic relationships within individual quadruples and (2) inadequately learning a unified representation of the contextual and temporal correlations among different quadruples. To overcome these limitations, we propose a novel Transformer-based reasoning model (dubbed ECEformer) for TKG to learn the Evolutionary Chain of Events (ECE). |
Zhiyu Fang; Shuai-Long Lei; Xiaobin Zhu; Chun Yang; Shi-Xue Zhang; Xu-Cheng Yin; Jingyan Qin; |
| 8 | LDRE: LLM-based Divergent Reasoning and Ensemble for Zero-Shot Composed Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods neglect that ZS-CIR is a typicalfuzzy retrieval task, where the semantics of the target image are not strictly defined by the query image and text. To overcome this limitation, this paper proposes a training-free LLM-based Divergent Reasoning and Ensemble (LDRE) method for ZS-CIR to capture diverse possible semantics of the composed result. |
Zhenyu Yang; Dizhan Xue; Shengsheng Qian; Weiming Dong; Changsheng Xu; |
| 9 | NativE: Multi-modal Knowledge Graph Completion in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing works usually focus on common modalities like image and text while neglecting the imbalanced distribution phenomenon of modal information. To address these issues, we propose a comprehensive framework NativE to achieve MMKGC in the wild. |
Yichi Zhang; Zhuo Chen; Lingbing Guo; Yajing Xu; Binbin Hu; Ziqi Liu; Wen Zhang; Huajun Chen; |
| 10 | Contrast Then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose the IMKGC task and a semantic neighbor retrieval-enhanced IMKGC framework CMR, where the contrast brings the helpful semantic neighbors close, and then the memorize supports semantic neighbor retrieval to enhance inference. |
Yu Zhao; Ying Zhang; Baohang Zhou; Xinying Qian; Kehui Song; Xiangrui Cai; |
| 11 | EditKG: Editing Knowledge Graph for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This imbalance is reflected by the long-tail phenomenon of item attributes, i.e., unpopular items usually lack more attributes compared to popular items. To tackle this problem, we propose a novel framework called EditKG: Editing Knowledge Graph for Recommendation, to balance attribute distribution of items via editing KGs. |
Gu Tang; Xiaoying Gan; Jinghe Wang; Bin Lu; Lyuwen Wu; Luoyi Fu; Chenghu Zhou; |
| 12 | Amazon-KG: A Knowledge Graph Enhanced Cross-Domain Recommendation Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we analyze the potential of KG applying in cross-domain recommendations, and describe the construction process of our dataset in detail. |
Yuhan Wang; Qing Xie; Mengzi Tang; Lin Li; Jingling Yuan; Yongjian Liu; |
| 13 | YAGO 4.5: A Large and Clean Knowledge Base with A Rich Taxonomy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we extend YAGO~4 with a large part of the Wikidata taxonomy — while respecting logical constraints and the distinction between classes and instances. |
Fabian M. Suchanek; Mehwish Alam; Thomas Bonald; Lihu Chen; Pierre-Henri Paris; Jules Soria; |
| 14 | Ranked List Truncation for Large Language Model-based Re-Ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study ranked list truncation (RLT) from a novel retrieve-then-re-rank perspective, where we optimize re-ranking by truncating the retrieved list (i.e., trim re-ranking candidates). |
Chuan Meng; Negar Arabzadeh; Arian Askari; Mohammad Aliannejadi; Maarten de Rijke; |
| 15 | Efficient Inverted Indexes for Approximate Retrieval Over Learned Sparse Representations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a novel organization of the inverted index that enables fast yet effective approximate retrieval over learned sparse embeddings. |
Sebastian Bruch; Franco Maria Nardini; Cosimo Rulli; Rossano Venturini; |
| 16 | GUITAR: Gradient Pruning Toward Fast Neural Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel graph searching framework to accelerate the searching in the fast neural ranking problem. |
Weijie Zhao; Shulong Tan; Ping Li; |
| 17 | Neural Passage Quality Estimation for Static Pruning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Most prominently, they can estimate the relevance of a passage or document to a user’s query. In this work, we depart from this direction by exploring whether neural networks can effectively predict which of a document’s passages are unlikely to be relevant to any query submitted to the search engine.We refer to this query-agnostic estimation of passage relevance as a passage’s quality.We find that our novel methods for estimating passage quality allow passage corpora to be pruned considerably while maintaining statistically equivalent effectiveness; our best methods can consistently prune >25\% of passages in a corpora, across various retrieval pipelines. |
Xuejun Chang; Debabrata Mishra; Craig Macdonald; Sean MacAvaney; |
| 18 | Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we conduct a detailed reproducibility study of Doc2Query- to better understand the trade-offs inherent to document expansion and filtering mechanisms. |
Watheq Mansour; Shengyao Zhuang; Guido Zuccon; Joel Mackenzie; |
| 19 | Unsupervised Cross-Domain Image Retrieval with Semantic-Attended Mixture-of-Experts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The resolution of these challenges relies on the accurate capture of domain-invariant semantic features by the model. Based on this consideration, we propose our Semantic-Attended Mixture of Experts (SA-MoE) model. |
Kai Wang; Jiayang Liu; Xing Xu; Jingkuan Song; Xin Liu; Heng Tao Shen; |
| 20 | Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This information makes the retriever estimate a higher relevance score. We conduct experiments to support this assertion.Findings in this paper reveal the potential impact of AI-generated images on retrieval and have implications for further research. |
Shicheng Xu; Danyang Hou; Liang Pang; Jingcheng Deng; Jun Xu; Huawei Shen; Xueqi Cheng; |
| 21 | COMI: COrrect and MItigate Shortcut Learning Behavior in Deep Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle these, in this paper, we propose COMI, a novel method to COrrect and MItigate shortcut learning behavior. |
Lili Zhao; Qi Liu; Linan Yue; Wei Chen; Liyi Chen; Ruijun Sun; Chao Song; |
| 22 | Simple But Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In particular, we introduce a Dual Query Unification-based Composed Image Retrieval framework (DQU-CIR), whose backbone simply involves a VLP model’s image encoder and a text encoder. |
Haokun Wen; Xuemeng Song; Xiaolin Chen; Yinwei Wei; Liqiang Nie; Tat-Seng Chua; |
| 23 | Fine-grained Textual Inversion Network for Zero-Shot Composed Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their significant progress, their coarse-grained textual inversion may be insufficient to capture the full content of the image accurately. To overcome this issue, in this work, we propose a novel Fine-grained Textual Inversion Network for ZS-CIR, named FTI4CIR. |
Haoqiang Lin; Haokun Wen; Xuemeng Song; Meng Liu; Yupeng Hu; Liqiang Nie; |
| 24 | The Treatment of Ties in Rank-Biased Overlap Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we propose a generalized formulation for RBO to handle ties, thanks to which we complete the original definitions by showing how to perform prefix evaluation. |
Matteo Corsi; Juli\'{a}n Urbano; |
| 25 | Uncontextualized Significance Considered Dangerous Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We examine the context of significance tests in offline retrieval experiments. |
Nicola Ferro; Mark Sanderson; |
| 26 | Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While fairness-only measures have been studied extensively, we look into whether joint measures can be trusted. We collect all joint evaluation measures of RS relevance and fairness, and ask: How much do they agree with each other? |
Theresia Veronika Rampisela; Tuukka Ruotsalo; Maria Maistro; Christina Lioma; |
| 27 | What Matters in A Measure? A Perspective from Large-Scale Search Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For example, we must also consider how much a metric costs; how robust it is to the happenstance of sampling; whether it is debuggable; and what activities are incentivised when a metric is taken as a goal.In this perspective paper we discuss what makes a search metric successful in large-scale settings, including factors which are not often canvassed in IR research but which are important in real-world use. We illustrate this with examples, including from industrial settings, and offer suggestions for metrics as part of a working system. |
Paul Thomas; Gabriella Kazai; Nick Craswell; Seth Spielman; |
| 28 | CIRAL: A Test Collection for CLIR Evaluations in African Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present CIRAL, a test collection for cross-lingual retrieval with English queries and passages in four African languages: Hausa, Somali, Swahili, and Yoruba. |
Mofetoluwa Adeyemi; Akintunde Oladipo; Xinyu Zhang; David Alfonso-Hermelo; Mehdi Rezagholizadeh; Boxing Chen; Abdul-Hakeem Omotayo; Idris Abdulmumin; Naome A. Etori; Toyib Babatunde Musa; Samuel Fanijo; Oluwabusayo Olufunke Awoyomi; Saheed Abdullahi Salahudeen; Labaran Adamu Mohammed; Daud Olamide Abolade; Falalu Ibrahim Lawan; Maryam Sabo Abubakar; Ruqayya Nasir Iro; Amina Imam Abubakar; Shafie Abdi Mohamed; Hanad Mohamud Mohamed; Tunde Oluwaseyi Ajayi; Jimmy Lin; |
| 29 | ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While methods and systems continue evolving, existing test collections for this task exhibit shortcomings, particularly suffering from lexical bias in pooling and limited to keyword-style queries for evaluation. To address these limitations, in this paper, we construct ACORDAR 2.0, a new test collection for this task which is also the largest to date. |
Qiaosheng Chen; Weiqing Luo; Zixian Huang; Tengteng Lin; Xiaxia Wang; Ahmet Soylu; Basil Ell; Baifan Zhou; Evgeny Kharlamov; Gong Cheng; |
| 30 | Browsing and Searching Metadata of TREC Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With a particular focus on the run files, the paper motivates the requirements for better access to TREC metadata and details the concepts, the resources, the corresponding implementations, and possible use cases. |
Timo Breuer; Ellen M. Voorhees; Ian Soboroff; |
| 31 | Large Language Models for Intent-Driven Session Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, they can only learn user intents in the latent space, which further restricts the model’s transparency. To ease these issues, we propose a simple yet effective paradigm for ISR motivated by the advanced reasoning capability of large language models (LLMs). |
Zhu Sun; Hongyang Liu; Xinghua Qu; Kaidong Feng; Yan Wang; Yew Soon Ong; |
| 32 | Sequential Recommendation with Latent Relations Based on Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items. |
Shenghao Yang; Weizhi Ma; Peijie Sun; Qingyao Ai; Yiqun Liu; Mingchen Cai; Min Zhang; |
| 33 | Enhancing Sequential Recommenders with Augmented Knowledge from Aligned Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, integrating LLMs into sequential recommender systems comes with its own challenges, including inadequate representation of sequential behavior patterns and long inference latency. In this paper, we propose SeRALM (Enhancing <u>Se</u>quential <u>R</u>ecommenders with Augmented Knowledge from <u>A</u>ligned Large <u>L</u>anguage <u>M</u>odels) to address these challenges. |
Yankun Ren; Zhongde Chen; Xinxing Yang; Longfei Li; Cong Jiang; Lei Cheng; Bo Zhang; Linjian Mo; Jun Zhou; |
| 34 | IDGenRec: LLM-RecSys Alignment with Textual ID Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To better align LLMs with recommendation needs, we propose IDGenRec, representing each item as a unique, concise, semantically rich, platform-agnostic textual ID using human language tokens. |
Juntao Tan; Shuyuan Xu; Wenyue Hua; Yingqiang Ge; Zelong Li; Yongfeng Zhang; |
| 35 | Data-efficient Fine-tuning for LLM-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While coreset selection is closely related to the proposed task, existing coreset selection methods often rely on suboptimal heuristic metrics or entail costly optimization on large-scale recommendation data. To tackle these issues, we introduce two primary objectives for the data pruning task in the context of LLM-based recommendation: 1) high accuracy aims to identify the influential samples that can lead to high overall performance; and 2) high efficiency underlines the low costs of the data pruning process. |
Xinyu Lin; Wenjie Wang; Yongqi Li; Shuo Yang; Fuli Feng; Yinwei Wei; Tat-Seng Chua; |
| 36 | Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we leverage language understanding capabilities and adapt large language models (LLMs) as an environment (LE) to enhance RL-based recommenders. |
Jie Wang; Alexandros Karatzoglou; Ioannis Arapakis; Joemon M. Jose; |
| 37 | OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces OpenP5, an open-source platform designed as a resource to facilitate the development, training, and evaluation of LLM-based generative recommender systems for research purposes. |
Shuyuan Xu; Wenyue Hua; Yongfeng Zhang; |
| 38 | Fair Sequential Recommendation Without User Demographics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore a critical question: how can we build a fair sequential recommendation system without even knowing user demographics? |
Huimin Zeng; Zhankui He; Zhenrui Yue; Julian McAuley; Dong Wang; |
| 39 | CaDRec: Contextualized and Debiased Recommender Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a contextualized and debiased recommender model (CaDRec). |
Xinfeng Wang; Fumiyo Fukumoto; Jin Cui; Yoshimi Suzuki; Jiyi Li; Dongjin Yu; |
| 40 | Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we consider multifactorial selection bias in RSs. |
Jin Huang; Harrie Oosterhuis; Masoud Mansoury; Herke van Hoof; Maarten de Rijke; |
| 41 | Adaptive Fair Representation Learning for Personalized Fairness in Recommendations Via Information Alignment Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy. In this paper, we propose a novel Adaptive Fair Representation Learning (AFRL) model, which achieves a real personalized fairness due to its advantage of training only one model to adaptively serve different fairness requirements during inference phase. |
Xinyu Zhu; Lilin Zhang; Ning Yang; |
| 42 | Configurable Fairness for New Item Recommendation Considering Entry Time of Items Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a multi-degree new-item exposure fairness definition, which considers item entry-time, and also is configurable regarding different fairness requirements. |
Huizhong Guo; Dongxia Wang; Zhu Sun; Haonan Zhang; Jinfeng Li; Jie Zhang; |
| 43 | Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose Distributionally Robust Fair Optimization (DRFO), which minimizes the worst-case unfairness over all potential probability distributions of missing sensitive attributes instead of the reconstructed one to account for the impact of the reconstruction errors. |
Tianhao Shi; Yang Zhang; Jizhi Zhang; Fuli Feng; Xiangnan He; |
| 44 | Generative Retrieval Via Term Set Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: What’s worse, during decoding, the model can only perceive preceding tokens in the DocID while being blind to subsequent ones, hence is prone to make such errors. To address this problem, we present a novel framework for generative retrieval, dubbed Term-Set Generation (TSGen). |
Peitian Zhang; Zheng Liu; Yujia Zhou; Zhicheng Dou; Fangchao Liu; Zhao Cao; |
| 45 | Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation Through Simultaneous Decoding Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces PAG-a novel optimization and decoding approach that guides autoregressive generation of document identifiers in generative retrieval models through simultaneous decoding. |
Hansi Zeng; Chen Luo; Hamed Zamani; |
| 46 | Large Language Models and Future of Information Retrieval: Opportunities and Challenges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Will LLMs eventually replace an IR system? In this perspective paper, we examine these questions and provide provisional answers to them. |
ChengXiang Zhai; |
| 47 | GraphGPT: Graph Instruction Tuning for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the success of large language models (LLMs), we aim to create a graph-oriented LLM capable of exceptional generalization across various datasets and tasks without relying on downstream graph data. |
Jiabin Tang; Yuhao Yang; Wei Wei; Lei Shi; Lixin Su; Suqi Cheng; Dawei Yin; Chao Huang; |
| 48 | Instruction-based Hypergraph Pretraining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel pretraining framework named Instruction-based Hypergraph Pretraining. |
Mingdai Yang; Zhiwei Liu; Liangwei Yang; Xiaolong Liu; Chen Wang; Hao Peng; Philip S. Yu; |
| 49 | LLM-enhanced Cascaded Multi-level Learning on Temporal Heterogeneous Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on two valuable and understudied issues related to THG learning: (a) How to capture the specific evolutionary characteristics of diverse temporal heterogeneous graphs? |
Fengyi Wang; Guanghui Zhu; Chunfeng Yuan; Yihua Huang; |
| 50 | Course Recommender Systems Need to Consider The Job Market Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper focuses on the perspective of academic researchers, working in collaboration with the industry, aiming to develop a course recommender system that incorporates job market skill demands. |
Jibril Frej; Anna Dai; Syrielle Montariol; Antoine Bosselut; Tanja K\{a}ser; |
| 51 | Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed), which leverages the pretrain-finetune learning paradigm to enhance accuracy for rare diseases. |
Zihao Zhao; Yi Jing; Fuli Feng; Jiancan Wu; Chongming Gao; Xiangnan He; |
| 52 | MIRROR: A Multi-View Reciprocal Recommender System for Online Recruitment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, in RRSs, users may exhibit varying preferences when acting in different roles, and how to effectively model users from multiple perspectives remains a substantial problem. To solve the above challenges, in this paper, we propose a novel MultI-view Reciprocal Recommender system for Online Recruitment (MIRROR). |
Zhi Zheng; Xiao Hu; Shanshan Gao; Hengshu Zhu; Hui Xiong; |
| 53 | MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Accordingly, the existing body of work on news recommendation suffers from a lack of publicly available multilingual benchmarks that would catalyze development of news recommenders effective in multilingual settings and for low-resource languages. Aiming to fill this gap, we introduce xMIND, an open, multilingual news recommendation dataset derived from the English MIND dataset using machine translation, covering a set of 14 linguistically and geographically diverse languages, with digital footprints of varying sizes. |
Andreea Iana; Goran Glava\v{s}; Heiko Paulheim; |
| 54 | MealRec+: A Meal Recommendation Dataset with Meal-Course Affiliation for Personalization and Healthiness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To pave the way for meal recommendation research, we introduce a new benchmark dataset called MealRec^+. |
Ming Li; Lin Li; Xiaohui Tao; Jimmy Xiangji Huang; |
| 55 | Negative Sampling Techniques for Dense Passage Retrieval in A Multilingual Setting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We examine techniques that were introduced for finding hard negatives in a monolingual setting and reproduce them in a multilingual setting. We discover a gap amongst these techniques that we fill by proposing a novel clustered training method. |
Thilina Chaturanga Rajapakse; Andrew Yates; Maarten de Rijke; |
| 56 | Steering Large Language Models for Cross-lingual Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In today’s digital age, accessing information across language barriers poses a significant challenge, with conventional search systems often struggling to interpret and retrieve multilingual content accurately. Addressing this issue, our study introduces a novel integration of applying Large Language Models (LLMs) as Cross-lingual Readers in information retrieval systems, specifically targeting the complexities of cross-lingual information retrieval (CLIR). |
Ping Guo; Yubing Ren; Yue Hu; Yanan Cao; Yunpeng Li; Heyan Huang; |
| 57 | Multilingual Meta-Distillation Alignment for Semantic Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, on top of an optimization-based Model-Agnostic Meta-Learner (MAML), we propose a data-efficient meta-distillation approach: MAML-Align,1 specifically for low-resource multilingual semantic retrieval. |
Meryem M’hamdi; Jonathan May; Franck Dernoncourt; Trung Bui; Seunghyun Yoon; |
| 58 | DAC: Quantized Optimal Transport Reward-based Reinforcement Learning Approach to Detoxify Query Auto-Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (2) These approaches often view detoxification through a binary lens where all text labeled as toxic is undesirable, and non-toxic is considered desirable. To address these limitations, we propose DAC, an intuitive and efficient reinforcement learning-based model to detoxify QAC. |
Aishwarya Maheswaran; Kaushal Kumar Maurya; Manish Gupta; Maunendra Sankar Desarkar; |
| 59 | Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, early fusion of entity information is critical for sentiment classification. In this paper, we propose an Enhanced Packed Marker with Entity Information (EPMEI) framework for ASTE task to address the above limitations of the existing works. |
You Li; Xupeng Zeng; Yixiao Zeng; Yuming Lin; |
| 60 | Exogenous and Endogenous Data Augmentation for Low-Resource Complex Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel <u>d</u>ata <u>a</u>ugmentation method E2DA from both <u>e</u>xogenous and <u>e</u>ndogenous perspectives. |
Xinghua Zhang; Gaode Chen; Shiyao Cui; Jiawei Sheng; Tingwen Liu; Hongbo Xu; |
| 61 | C-Pack: Packed Resources For General Chinese Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce C-Pack, a package of resources that significantly advances the field of general text embeddings for Chinese. |
Shitao Xiao; Zheng Liu; Peitian Zhang; Niklas Muennighoff; Defu Lian; Jian-Yun Nie; |
| 62 | QuanTemp: A Real-world Open-domain Benchmark for Fact-checking Numerical Claims Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This addresses the challenge of verifying real-world numerical claims, which are complex and often lack precise information, a gap not filled by existing works that mainly focus on synthetic claims. We evaluate and quantify these gaps in existing solutions for the task of verifying numerical claims. |
Venktesh V; Abhijit Anand; Avishek Anand; Vinay Setty; |
| 63 | ACE-2005-PT: Corpus for Event Extraction in Portuguese Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces ACE-2005-PT, a corpus created by translating ACE-2005 into Portuguese, with European and Brazilian variants. |
Lu\'{\i}s Filipe Cunha; Purifica\c{c}\~{a}o Silvano; Ricardo Campos; Al\'{\i}pio Jorge; |
| 64 | Who To Align With: Feedback-Oriented Multi-Modal Alignment in Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A plug-and-play framework is presented, called FEedback-orienTed mulTi-modal aLignmEnt (FETTLE). |
Yang Li; Qi’Ao Zhao; Chen Lin; Jinsong Su; Zhilin Zhang; |
| 65 | Multimodality Invariant Learning for Multimedia-Based New Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, how to construct environments from finite user behavior training data to generalize any modality missing is challenging. To tackle this issue, we propose a novel Multimodality Invariant Learning reCommendation (a.k.a. MILK) framework. |
Haoyue Bai; Le Wu; Min Hou; Miaomiao Cai; Zhuangzhuang He; Yuyang Zhou; Richang Hong; Meng Wang; |
| 66 | IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt foundation models for recommendation tasks, most research prioritizes parameter efficiency, often overlooking critical factors like GPU memory efficiency and training speed. Addressing this gap, our paper introduces IISAN (<u>I</u>ntra- and <u>I</u>nter-modal <u>S</u>ide <u>A</u>dapted <u>N</u>etwork for Multimodal Representation)1 a simple plug-and-play architecture using a Decoupled PEFT structure and exploiting both intra- and inter-modal adaptation.IISAN matches the performance of full fine-tuning (FFT) and state-of-the-art PEFT. |
Junchen Fu; Xuri Ge; Xin Xin; Alexandros Karatzoglou; Ioannis Arapakis; Jie Wang; Joemon M. Jose; |
| 67 | EEG-SVRec: An EEG Dataset with User Multidimensional Affective Engagement Labels in Short Video Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work presents a novel perspective for enhancing short video recommendation by leveraging the rich information contained in EEG signals and multidimensional affective engagement scores, paving the way for future research in short video recommendation systems. |
Shaorun Zhang; Zhiyu He; Ziyi Ye; Peijie Sun; Qingyao Ai; Min Zhang; Yiqun Liu; |
| 68 | Dataset and Models for Item Recommendation Using Multi-Modal User Interactions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We study the case of multi-modal user interactions in a setting where users engage with a service provider through multiple channels (website and call center). |
Simone Borg Bruun; Krisztian Balog; Maria Maistro; |
| 69 | The Power of Noise: Redefining Retrieval for RAG Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We focus, in particular, on the type of passages IR systems within a RAG solution should retrieve. |
Florin Cuconasu; Giovanni Trappolini; Federico Siciliano; Simone Filice; Cesare Campagnano; Yoelle Maarek; Nicola Tonellotto; Fabrizio Silvestri; |
| 70 | IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although the Retrieval-Augmented Generation (RAG) paradigms can use external knowledge to enhance and ground the outputs of Large Language Models (LLMs) to mitigate generative hallucinations and static knowledge base problems, they still suffer from limited flexibility in adopting Information Retrieval (IR) systems with varying capabilities, constrained interpretability during the multi-round retrieval process, and a lack of end-to-end optimization. To address these challenges, we propose a novel LLM-centric approach, IM-RAG, that integrates IR systems with LLMs to support multi-round RAG through learning Inner Monologues (IM, i.e., the human inner voice that narrates one’s thoughts). |
Diji Yang; Jinmeng Rao; Kezhen Chen; Xiaoyuan Guo; Yawen Zhang; Jie Yang; Yi Zhang; |
| 71 | Towards A Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces uRAG-a framework with a unified retrieval engine that serves multiple downstream retrieval-augmented generation (RAG) systems. |
Alireza Salemi; Hamed Zamani; |
| 72 | Optimization Methods for Personalizing Large Language Models Through Retrieval Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose the first attempt to optimize the retrieval models that deliver a limited number of personal documents to large language models for the purpose of personalized generation. |
Alireza Salemi; Surya Kallumadi; Hamed Zamani; |
| 73 | FeB4RAG: Evaluating Federated Search in The Context of Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing datasets, such as those developed in the past TREC FedWeb tracks, predate the RAG paradigm shift and lack representation of modern information retrieval challenges.To bridge this gap, we present FeB4RAG, a novel dataset specifically designed for federated search within RAG frameworks. |
Shuai Wang; Ekaterina Khramtsova; Shengyao Zhuang; Guido Zuccon; |
| 74 | Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we tackle two key challenges in ESC: enhancing contextually relevant and empathetic response generation through dynamic demonstration retrieval, and advancing cognitive understanding to grasp implicit mental states comprehensively. |
Zhe Xu; Daoyuan Chen; Jiayi Kuang; Zihao Yi; Yaliang Li; Ying Shen; |
| 75 | Broadening The View: Demonstration-augmented Prompt Learning for Conversational Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. |
Huy Dao; Yang Deng; Dung D. Le; Lizi Liao; |
| 76 | Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Overall, LAPS introduces a new method to leverage LLMs to create realistic personalized conversational data more efficiently and effectively than previous methods. |
Hideaki Joko; Shubham Chatterjee; Andrew Ramsay; Arjen P. de Vries; Jeff Dalton; Faegheh Hasibi; |
| 77 | Towards Human-centered Proactive Conversational Agents Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address the issue by establishing a new taxonomy concerning three key dimensions of human-centered PCAs, namely Intelligence, Adaptivity, and Civility. |
Yang Deng; Lizi Liao; Zhonghua Zheng; Grace Hui Yang; Tat-Seng Chua; |
| 78 | TREC IKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A total of 344 turns with approximately 26,000 passages are provided as assessments on relevance, as well as additional assessments on generated responses over four key dimensions: relevance, completeness, groundedness, and naturalness. The collection challenges CSAs to efficiently navigate diverse personal contexts, elicit pertinent persona information, and employ context for relevant conversations.The integration of a PTKB and the emphasis on decisional search tasks contribute to the uniqueness of this test collection, making it an essential benchmark for advancing research in conversational and interactive knowledge assistants. |
Mohammad Aliannejadi; Zahra Abbasiantaeb; Shubham Chatterjee; Jeffrey Dalton; Leif Azzopardi; |
| 79 | ProCIS: A Benchmark for Proactive Retrieval in Conversations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we introduce a large-scale dataset for proactive document retrieval that consists of over 2.8 million conversations. |
Chris Samarinas; Hamed Zamani; |
| 80 | An Empirical Analysis on Multi-turn Conversational Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We, for the first time, propose a comprehensive experimental evaluation for existing SOTA multi-turn CRSs to investigate three research questions: (1) reproducibility – are the designed components beneficial to target multi-turn CRSs? |
Lu Zhang; Chen Li; Yu Lei; Zhu Sun; Guanfeng Liu; |
| 81 | UGNCL: Uncertainty-Guided Noisy Correspondence Learning for Efficient Cross-Modal Matching Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Although some recent methods have attempted to address this problem, they still face two challenging issues: 1) unreliable data division for training inefficiency and 2) unstable prediction for matching failure. To address these problems, we propose an efficient Uncertainty-Guided Noisy Correspondence Learning (UGNCL) framework to achieve noise-robust cross-modal matching. |
Quanxing Zha; Xin Liu; Yiu-ming Cheung; Xing Xu; Nannan Wang; Jianjia Cao; |
| 82 | Universal Adversarial Perturbations for Vision-Language Pre-trained Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel black-box method to generate Universal Adversarial Perturbations (UAPs), which is so called the <u>E</u>ffective and <u>T</u>ransferable <u>U</u>niversal Adversarial Attack (ETU), aiming to mislead a variety of existing VLP models in a range of downstream tasks. |
Peng-Fei Zhang; Zi Huang; Guangdong Bai; |
| 83 | Semi-supervised Prototype Semantic Association Learning for Robust Cross-modal Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The key to essentially addressing the SS-CMR task is to solve the semantic association and modality heterogeneity problems. To address these issues, in this paper, we propose a novel semi-supervised cross-modal retrieval method, namely Semi-supervised Prototype Semantic Association Learning (SPAL) for robust cross-modal retrieval. |
Junsheng Wang; Tiantian Gong; Yan Yan; |
| 84 | Self-Improving Teacher Cultivates Better Student: Distillation Calibration for Multimodal Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The comprehension of students is highly reliant on the teacher models. To address this issue, we propose a novel Multimodal Distillation Calibration framework (MmDC). |
Xinwei Li; Li Lin; Shuai Wang; Chen Qian; |
| 85 | M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we introduce M3oE, an adaptive Multi-domain Multi-task Mixture-of-Experts recommendation framework. |
Zijian Zhang; Shuchang Liu; Jiaao Yu; Qingpeng Cai; Xiangyu Zhao; Chunxu Zhang; Ziru Liu; Qidong Liu; Hongwei Zhao; Lantao Hu; Peng Jiang; Kun Gai; |
| 86 | Hypergraph Convolutional Network for User-Oriented Fairness in Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take the example of a typical service system, the recommender system, to investigate how to identify and tackle fairness issues within the service system. |
Zhongxuan Han; Chaochao Chen; Xiaolin Zheng; Li Zhang; Yuyuan Li; |
| 87 | DHMAE: A Disentangled Hypergraph Masked Autoencoder for Group Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although some existing powerful deep learning models have achieved improved performance, various aspects remain unexplored: (1) Most existing models using contrastive learning tend to rely on high-quality data augmentation which requires precise contrastive view generation; (2) There is multifaceted natural noise in group recommendation, and additional noise is introduced during data augmentation; (3) Most existing hypergraph neural network-based models over-entangle the information of members and items, ignoring their unique characteristics. In light of this, we propose a highly effective <u>D</u>isentangled <u>H</u>ypergraph <u>M</u>asked <u>A</u>uto <u>E</u>ncoder-enhanced method for group recommendation (DHMAE), combining a disentangled hypergraph neural network with a graph masked autoencoder. |
Yingqi Zhao; Haiwei Zhang; Qijie Bai; Changli Nie; Xiaojie Yuan; |
| 88 | Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To leverage and verify the existence of such short-cuts, we propose a plug-and-play two-step repetition-exploration (TREx) framework that treats repeat items and explores items separately, where we design a simple yet highly effective repetition module to ensure high accuracy, while two exploration modules target optimizing only beyond-accuracy metrics.Experiments are performed on two widely-used datasets w.r.t. a range of beyond-accuracy metrics, viz. five fairness metrics and three diversity metrics. |
Ming Li; Yuanna Liu; Sami Jullien; Mozhdeh Ariannezhad; Andrew Yates; Mohammad Aliannejadi; Maarten de Rijke; |
| 89 | NFARec: A Negative Feedback-Aware Recommender Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a negative feedback-aware recommender model (NFARec) that maximizes the leverage of negative feedback. |
Xinfeng Wang; Fumiyo Fukumoto; Jin Cui; Yoshimi Suzuki; Dongjin Yu; |
| 90 | Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this direct transfer can introduce noise to the target behavior in recommendation, due to variations in user attention across different behaviors. To address this issue, this paper introduces a novel approach, Behavior-Contextualized Item Preference Modeling (BCIPM), for multi-behavior recommendation. |
Mingshi Yan; Fan Liu; Jing Sun; Fuming Sun; Zhiyong Cheng; Yahong Han; |
| 91 | AutoDCS: Automated Decision Chain Selection in Deep Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We call this a full decision chain constraint and argue that it may be too strict by ignoring that different types of behavioral knowledge have varying importance for different users. In this paper, we propose a novel automated decision chain selection (AutoDCS) framework to relax this constraint, which can consider each user’s unique decision dependencies and select a reasonable set of behavioral knowledge to activate for the prediction of target behavior. |
Dugang Liu; Shenxian Xian; Yuhao Wu; Chaohua Yang; Xing Tang; Xiuqiang He; Zhong Ming; |
| 92 | Adaptive In-Context Learning with Large Language Models for Bundle Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the reasoning capabilities of large language models (LLMs), we propose an adaptive in-context learning paradigm, which allows LLMs to draw tailored lessons from related sessions as demonstrations, enhancing the performance on target sessions. |
Zhu Sun; Kaidong Feng; Jie Yang; Xinghua Qu; Hui Fang; Yew-Soon Ong; Wenyuan Liu; |
| 93 | EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, research in this field faces challenges, including the lack of user-friendly frameworks, inconsistent evaluation metrics, and difficulties in reproducing existing studies. To tackle these issues, we introduce EasyRL4Rec, an easy-to-use code library designed specifically for RL-based RSs. |
Yuanqing Yu; Chongming Gao; Jiawei Chen; Heng Tang; Yuefeng Sun; Qian Chen; Weizhi Ma; Min Zhang; |
| 94 | SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: After presenting the dataset’s collection procedure and basic statistics, we propose three tasks that are rarely available to conduct using existing RS datasets: impressions-aware click prediction, users’ propensity scores prediction, and construction of recommendations proportional to the users’ propensity scores. |
Patrik Dokoupil; Ladislav Peska; Ludovico Boratto; |
| 95 | Modeling User Fatigue for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec). |
Nian Li; Xin Ban; Cheng Ling; Chen Gao; Lantao Hu; Peng Jiang; Kun Gai; Yong Li; Qingmin Liao; |
| 96 | Characterizing Information Seeking Processes with Multiple Physiological Signals Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Leveraging the advances in sensing technologies, our study aims to characterize user behaviors with physiological signals, particularly in relation to cognitive load, affective arousal, and valence. |
Kaixin Ji; Danula Hettiachchi; Flora D. Salim; Falk Scholer; Damiano Spina; |
| 97 | To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Inspired by the success of the Neural Hawkes Process (NHP) in modeling temporal dependencies in sequences, this paper proposes a novel neural Hawkes process model to capture the temporal dependencies between historical user browsing and querying actions. |
Zhongxiang Sun; Zihua Si; Xiao Zhang; Xiaoxue Zang; Yang Song; Hongteng Xu; Jun Xu; |
| 98 | UniSAR: Modeling User Transition Behaviors Between Search and Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a framework named UniSAR that effectively models the different types of fine-grained behavior transitions for providing users a Unified Search And Recommendation service. |
Teng Shi; Zihua Si; Jun Xu; Xiao Zhang; Xiaoxue Zang; Kai Zheng; Dewei Leng; Yanan Niu; Yang Song; |
| 99 | Explainability for Transparent Conversational Information-Seeking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By exploring transparency across explanation type, quality, and presentation mode, this research aims to bridge the gap between system-generated responses and responses verifiable by the user. |
Weronika \L{}ajewska; Damiano Spina; Johanne Trippas; Krisztian Balog; |
| 100 | Evaluating Search System Explainability with Psychometrics and Crowdsourcing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we use psychometrics and crowdsourcing to identify human-centered factors of explainability in Web search systems and introduce SSE (Search System Explainability), an evaluation metric for explainable IR (XIR) search systems. |
Catherine Chen; Carsten Eickhoff; |
| 101 | Sequential Recommendation with Collaborative Explanation Via Mutual Information Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new explanation type, namely, collaborative explanation, into sequential recommendation, allowing a unified approach for modeling user actions and assessing the performance of both recommendation and explanation. |
Yi Yu; Kazunari Sugiyama; Adam Jatowt; |
| 102 | Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Additionally, current works use GPT-4 to only predict the correct option without providing any explanation and thus do not provide any insight into the thinking process and reasoning used by GPT-4 or other LLMs. Therefore, we introduce a new domain-specific error taxonomy derived from collaboration with medical students. |
Soumyadeep Roy; Aparup Khatua; Fatemeh Ghoochani; Uwe Hadler; Wolfgang Nejdl; Niloy Ganguly; |
| 103 | Hierarchical Semantics Alignment for 3D Human Motion Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the aforementioned challenges, we propose a novel Hierarchical Semantics Alignment (HSA) framework for text-to-3D human motion retrieval. |
Yang Yang; Haoyu Shi; Huaiwen Zhang; |
| 104 | Enhancing Dataset Search with Compact Data Snippets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, current metadata-based approaches have revealed shortcomings due to the low quality and availability of dataset metadata, while the magnitude and heterogeneity of actual data hindered the development of content-based solutions. To address these challenges, we propose to convert different formats of structured data into a unified form, from which we extract a compact data snippet that indicates the relevance of the whole data. |
Qiaosheng Chen; Jiageng Chen; Xiao Zhou; Gong Cheng; |
| 105 | When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Besides, the large amount of parameters in LLMs leads to huge time and computation consumption by fine-tuning. To address these two problems, we propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA. |
Qidong Liu; Xian Wu; Xiangyu Zhao; Yuanshao Zhu; Derong Xu; Feng Tian; Yefeng Zheng; |
| 106 | Resources for Combining Teaching and Research in Information Retrieval Coursework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The full-day workshop had two calls for contributions: the first call aimed at scientific contributions to building, operating, and evaluating search engines cooperatively and the cooperative use of the web as a resource for researchers and innovators. |
Maik Fr\{o}be; Harrisen Scells; Theresa Elstner; Christopher Akiki; Lukas Gienapp; Jan Heinrich Reimer; Sean MacAvaney; Benno Stein; Matthias Hagen; Martin Potthast; |
| 107 | OEHR: An Orthopedic Electronic Health Record Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents OEHR, a healthcare benchmark dataset in Orthopedics, sourced from the EHR of real hospitals. |
Yibo Xie; Kaifan Wang; Jiawei Zheng; Feiyan Liu; Xiaoli Wang; Guofeng Huang; |
| 108 | SuicidEmoji: Derived Emoji Dataset and Tasks for Suicide-Related Social Content Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we build a derived suicide-related emoji dataset named SuicidEmoji, which contains 25k emoji posts (2,329 suicide-related posts and 22,722 posts for the control group users) filtered from about 1.3 million crawled Reddit data. |
Tianlin Zhang; Kailai Yang; Shaoxiong Ji; Boyang Liu; Qianqian Xie; Sophia Ananiadou; |
| 109 | A Large Scale Test Corpus for Semantic Table Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing table repositories and benchmarks are limited in their ability to test retrieval methods for table search tasks. Thus, to close this gap, we introduce a novel dataset for query-by-example Semantic Table Search. |
Aristotelis Leventidis; Martin Pek\'{a}r Christensen; Matteo Lissandrini; Laura Di Rocco; Katja Hose; Ren\'{e}e J. Miller; |
| 110 | JDivPS: A Diversified Product Search Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such limitations may lead to irreproducible experimental results and unreliable conclusions, restricting the development of this field. To address these problems, this paper introduces a novel dataset JDivPS for diversified product search. |
Zhirui Deng; Zhicheng Dou; Yutao Zhu; Xubo Qin; Pengchao Cheng; Jiangxu Wu; Hao Wang; |
| 111 | An E-Commerce Dataset Revealing Variations During Sales Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, models must readjust themselves once sales have concluded in order to eliminate any effects caused by the sales events, leading to further regret. To address these limitations, we introduce a long-term E-Commerce search data set specifically designed to incubate LTR algorithms during such sales events, with the objective of advancing the capabilities of E-Commerce search engines. |
Jianfu Zhang; Qingtao Yu; Yizhou Chen; Guoliang Zhou; Yawen Liu; Yawei Sun; Chen Liang; Guangda Huzhang; Yabo Ni; Anxiang Zeng; Han Yu; |
| 112 | LADy 💃: A Benchmark Toolkit for Latent Aspect Detection Enriched with Backtranslation Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present LADy ᖡ, a Python-based benchmark toolkit to facilitate extracting aspects of products or services in reviews toward which customers target their opinions and sentiments. |
Farinam Hemmatizadeh; Christine Wong; Alice Yu; Hossein Fani; |
| 113 | DDPO: Direct Dual Propensity Optimization for Post-Click Conversion Rate Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To exploit the unclicked samples, we propose a Direct Dual Propensity Optimization (DDPO) framework to optimize the model directly in impression space with both clicked and unclicked samples. |
Hongzu Su; Lichao Meng; Lei Zhu; Ke Lu; Jingjing Li; |
| 114 | Deep Pattern Network for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, three critical challenges demand attention: the inclusion of unrelated items within patterns, data sparsity of patterns, and computational complexity arising from numerous patterns. To address these challenges, we introduce the Deep Pattern Network (DPN), designed to comprehensively leverage information from behavior patterns. |
Hengyu Zhang; Junwei Pan; Dapeng Liu; Jie Jiang; Xiu Li; |
| 115 | Counterfactual Ranking Evaluation with Flexible Click Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a new counterfactual estimator, called Interpol, that provides a tunable trade-off in the assumptions it makes, thus providing a novel ability to optimize the bias-variance trade-off. |
Alexander Buchholz; Ben London; Giuseppe Di Benedetto; Jan Malte Lichtenberg; Yannik Stein; Thorsten Joachims; |
| 116 | Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This implies that the advertisement cannot secure a better position and thus loses the opportunity to achieve higher utility in the subsequent ad allocation stage. Previous research often focused on one of the two stages, neglecting the two-stage problem, which may result in suboptimal outcomes.Therefore, this paper proposes a deep automated mechanism that integrates ad auction and allocation, ensuring both IC and Individual Rationality (IR) in the presence of externalities while maximizing revenue and GMV. |
Xuejian Li; Ze Wang; Bingqi Zhu; Fei He; Yongkang Wang; Xingxing Wang; |
| 117 | CWRCzech: 100M Query-Document Czech Click Dataset and Its Application to Web Relevance Ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present CWRCzech, Click Web Ranking dataset for Czech, a 100M query-document Czech click dataset for relevance ranking with user behavior data collected from search engine logs of Seznam.cz. |
Josef Von\'{a}sek; Milan Straka; Rostislav Kr\v{c}; Lenka Lasonov\'{a}; Ekaterina Egorova; Jana Strakov\'{a}; Jakub N\'{a}plava; |
| 118 | Exploring Multi-Scenario Multi-Modal CTR Prediction with A Large Scale Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, their scale is modest compared to real-world industrial datasets, hindering robust and comprehensive evaluation of complex models. To address these challenges, we introduce a large-scale <u>M</u>ulti-Scenario <u>M</u>ulti-Modal <u>C</u>TR dataset named AntM2 C, built from real industrial data from Alipay. |
Zhaoxin Huan; Ke Ding; Ang Li; Xiaolu Zhang; Xu Min; Yong He; Liang Zhang; Jun Zhou; Linjian Mo; Jinjie Gu; Zhongyi Liu; Wenliang Zhong; Guannan Zhang; Chenliang Li; Fajie Yuan; |
| 119 | AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, this paper aims to address the aforementioned gap by undertaking a comprehensive study of the over-correlation issue in graph collaborative filtering models. |
Wei Wu; Chao Wang; Dazhong Shen; Chuan Qin; Liyi Chen; Hui Xiong; |
| 120 | Exploring The Individuality and Collectivity of Intents Behind Interactions for Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite considerable progress, we argue that it still confronts the following challenges: firstly, these methods only capture the coarse-grained aspects of intent, ignoring the fact that user-item interactions will be affected by collective and individual factors (e.g., a user may choose a movie because of its high box office or because of his own unique preferences); secondly, modeling believable intent is severely hampered by implicit feedback, which is incredibly sparse and devoid of true semantics. To address these challenges, we propose a novel recommendation framework designated as Bilateral Intent-guided Graph Collaborative Filtering (BIGCF). |
Yi Zhang; Lei Sang; Yiwen Zhang; |
| 121 | Content-based Graph Reconstruction for Cold-start Item Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This idea, however, has not been applicable to the cold-start items, since cold nodes are isolated in the graph and thus do not take advantage of information exchange from neighboring nodes. Recently, there have been a few attempts to utilize graph convolutions on item-item or user-user attribute graphs to capture high-order collaborative signals for cold-start cases, but these approaches are still limited in that the item-item or user-user graph falls short in capturing the dynamics of user-item interactions, as their edges are constructed based on arbitrary and heuristic attribute similarity.In this paper, we introduce Content-based Graph Reconstruction for Cold-start item recommendation (CGRC), employing a masked graph autoencoder structure and multimodal contents to directly incorporate interaction-based high-order connectivity, applicable even in cold-start scenarios. |
Jinri Kim; Eungi Kim; Kwangeun Yeo; Yujin Jeon; Chanwoo Kim; Sewon Lee; Joonseok Lee; |
| 122 | SIGformer: Sign-aware Graph Transformer for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: In recommender systems, most graph-based methods focus on positive user feedback, while overlooking the valuable negative feedback. Integrating both positive and negative feedback … |
Sirui Chen; Jiawei Chen; Sheng Zhou; Bohao Wang; Shen Han; Chanfei Su; Yuqing Yuan; Can Wang; |
| 123 | TransGNN: Harnessing The Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose TransGNN, a novel model that integrates Transformer and GNN layers in an alternating fashion to mutually enhance their capabilities. |
Peiyan Zhang; Yuchen Yan; Xi Zhang; Chaozhuo Li; Senzhang Wang; Feiran Huang; Sunghun Kim; |
| 124 | Lightweight Embeddings for Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose Lightweight Embeddings for Graph Collaborative Filtering (LEGCF), a parameter-efficient embedding framework dedicated to GNN-based recommenders. |
Xurong Liang; Tong Chen; Lizhen Cui; Yang Wang; Meng Wang; Hongzhi Yin; |
| 125 | Leveraging LLMs for Unsupervised Dense Retriever Ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper we present Large Language Model Assisted Retrieval Model Ranking (LARMOR), an effective unsupervised approach that leverages LLMs for selecting which dense retriever to use on a test corpus (target). |
Ekaterina Khramtsova; Shengyao Zhuang; Mahsa Baktashmotlagh; Guido Zuccon; |
| 126 | Dimension Importance Estimation for Dense Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To empirically validate our hypothesis, we define a novel class of Dimension IMportance Estimators (DIME). Such models aim to determine how much each dimension of a high-dimensional representation contributes to the quality of the final ranking and provide an empirical method to select a subset of dimensions where to project the query and the documents. |
Guglielmo Faggioli; Nicola Ferro; Raffaele Perego; Nicola Tonellotto; |
| 127 | Graded Relevance Scoring of Written Essays with Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on the relevance trait, which measures the ability of the student to stay on-topic throughout the entire essay. |
Salam Albatarni; Sohaila Eltanbouly; Tamer Elsayed; |
| 128 | Scaling Laws For Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we investigate how scaling affects the performance of dense retrieval models. |
Yan Fang; Jingtao Zhan; Qingyao Ai; Jiaxin Mao; Weihang Su; Jia Chen; Yiqun Liu; |
| 129 | Diffusion Models for Generative Outfit Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To achieve these, we propose a generative outfit recommender model named DiFashion, which empowers exceptional diffusion models to accomplish the parallel generation of multiple fashion images. |
Yiyan Xu; Wenjie Wang; Fuli Feng; Yunshan Ma; Jizhi Zhang; Xiangnan He; |
| 130 | Collaborative Filtering Based on Diffusion Models: Unveiling The Potential of High-Order Connectivity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing diffusion model-based recommender systems do not explicitly leverage high-order connectivities that contain crucial collaborative signals for accurate recommendations. Addressing this gap, we propose textsfCF-Diff, a new diffusion model-based collaborative filtering (CF) method, which is capable of making full use of collaborative signals along with multi-hop neighbors. |
Yu Hou; Jin-Duk Park; Won-Yong Shin; |
| 131 | Denoising Diffusion Recommender Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this kind of denoising process poses significant challenges to the recommender model’s representation capacity to capture noise patterns. To address this issue, we propose Denoising Diffusion Recommender Model (DDRM), which leverages multi-step denoising process of diffusion models to robustify user and item embeddings from any recommender models. |
Jujia Zhao; Wang Wenjie; Yiyan Xu; Teng Sun; Fuli Feng; Tat-Seng Chua; |
| 132 | Graph Signal Diffusion Model for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we adapt standard diffusion model and propose a novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF). |
Yunqin Zhu; Chao Wang; Qi Zhang; Hui Xiong; |
| 133 | Multi-granular Adversarial Attacks Against Black-box Neural Ranking Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conventional attack methods employ perturbations at a single granularity, e.g., word or sentence level, to target documents. |
Yu-An Liu; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Yixing Fan; Xueqi Cheng; |
| 134 | Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To provide a more granular understanding of internal model decision-making processes, we propose the use of causal interventions to reverse engineer neural rankers, and demonstrate how mechanistic interpretability methods can be used to isolate components satisfying term-frequency axioms within a ranking model. |
Catherine Chen; Jack Merullo; Carsten Eickhoff; |
| 135 | A Reproducibility Study of PLAID Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we reproduce and fill in missing gaps from the original work. |
Sean MacAvaney; Nicola Tonellotto; |
| 136 | Systematic Evaluation of Neural Retrieval Models on The Touch\'{e} 2020 Argument Retrieval Subset of BEIR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our study, we focus on two experiments: (i) a black-box evaluation (i.e., no model retraining), incorporating a theoretical exploration using retrieval axioms, and (ii) a data denoising evaluation involving post-hoc relevance judgments. |
Nandan Thakur; Luiz Bonifacio; Maik Fr\{o}be; Alexander Bondarenko; Ehsan Kamalloo; Martin Potthast; Matthias Hagen; Jimmy Lin; |
| 137 | Resources for Brewing BEIR: Reproducible Reference Models and Statistical Analyses Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we provide reproducible reference implementations that cover learned dense and sparse models. |
Ehsan Kamalloo; Nandan Thakur; Carlos Lassance; Xueguang Ma; Jheng-Hong Yang; Jimmy Lin; |
| 138 | Optimal Transport Enhanced Cross-City Site Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, such solutions are not able to fulfill the demand for rapid development in modern business. Therefore, we aim to alleviate the data sparsity problem by effectively utilizing data across multiple cities and thereby propose a novel Optimal Transport enhanced Cross-city (OTC) framework for site recommendation. |
Xinhang Li; Xiangyu Zhao; Zihao Wang; Yang Duan; Yong Zhang; Chunxiao Xing; |
| 139 | Disentangled Contrastive Hypergraph Learning for Next POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: ii) Many existing methods have inadequately modeled the crucial cooperative associations between different aspects, hindering the ability to capture complementary recommendation effects during the learning process. To tackle these challenges, we propose a novel framework <u>D</u>isentangled <u>C</u>ontrastive <u>H</u>ypergraph <u>L</u>earning (DCHL) for next POI recommendation. |
Yantong Lai; Yijun Su; Lingwei Wei; Tianqi He; Haitao Wang; Gaode Chen; Daren Zha; Qiang Liu; Xingxing Wang; |
| 140 | Large Language Models for Next Point-of-Interest Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous methods are limited by their numerical nature and fail to address this challenge. In this paper, we propose a framework that uses pretrained Large Language Models (LLMs) to tackle this challenge. |
Peibo Li; Maarten de Rijke; Hao Xue; Shuang Ao; Yang Song; Flora D. Salim; |
| 141 | CLLP: Contrastive Learning Framework Based on Latent Preferences for Next POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a contrastive learning framework based on latent preferences (CLLP) for next POI recommendation, which models the latent preference distributions of users at each POI and then yield disentangled latent preference representations. |
Hongli Zhou; Zhihao Jia; Haiyang Zhu; Zhizheng Zhang; |
| 142 | OpenSiteRec: An Open Dataset for Site Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, there is no publicly available dataset so far and most existing approaches are limited to an extremely small scope of brands, which seriously hinders the research on site recommendation. Therefore, we collect, construct and release an open comprehensive dataset, namely OpenSiteRec, to facilitate and promote the research on site recommendation. |
Xinhang Li; Xiangyu Zhao; Yejing Wang; Yu Liu; Chong Chen; Cheng Long; Yong Zhang; Chunxiao Xing; |
| 143 | A Taxation Perspective for Fair Re-ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, both empirical and theoretical analyses indicate that the previous item-level tax policy cannot meet two ideal controllable requirements: (1) continuity, ensuring minor changes in tax rates result in small accuracy and fairness shifts; (2) controllability over accuracy loss, ensuring precise estimation of the accuracy loss under a specific tax rate. To overcome these challenges, we introduce a new fair re-ranking method named Tax-rank, which levies taxes based on the difference in utility between two items. |
Chen Xu; Xiaopeng Ye; Wenjie Wang; Liang Pang; Jun Xu; Tat-Seng Chua; |
| 144 | Fairness-Aware Exposure Allocation Via Adaptive Reranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we investigate how deploying adaptive re-ranking, which enables the discovery of additional potentially relevant documents in the re-ranking stage, can improve the exposure that a given group of documents receives in the final ranking. |
Thomas Jaenich; Graham McDonald; Iadh Ounis; |
| 145 | The Impact of Group Membership Bias on The Quality and Fairness of Exposure in Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Then, we provide a correction method for group bias that is based on the assumption that the utility score of items in different groups comes from the same distribution. This assumption has two potential issues of sparsity and equality-instead-of-equity; we use an amortized approach to address these. |
Ali Vardasbi; Maarten de Rijke; Fernando Diaz; Mostafa Dehghani; |
| 146 | Optimizing Learning-to-Rank Models for Ex-Post Fair Relevance Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Prior studies introduced algorithms to train stochastic ranking models, such as the Plackett-Luce ranking model, that maximize expected ranking utility while achieving fairness in expectation (ex-ante fairness). |
Sruthi Gorantla; Eshaan Bhansali; Amit Deshpande; Anand Louis; |
| 147 | Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a new learning objective based on a backdoor adjustment. |
Dan Luo; Lixin Zou; Qingyao Ai; Zhiyu Chen; Chenliang Li; Dawei Yin; Brian D. Davison; |
| 148 | Unbiased Learning to Rank Meets Reality: Lessons from Baidu’s Large-Scale Search Dataset Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We find that standard unbiased learning-to-rank techniques robustly improve click predictions but struggle to consistently improve ranking performance, especially considering the stark differences obtained by choice of ranking loss and query-document features. |
Philipp Hager; Romain Deffayet; Jean-Michel Renders; Onno Zoeter; Maarten de Rijke; |
| 149 | Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to The Scenario Context Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enhance the model’s capacity to capture user interests across scenarios, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs a fine-grained method for multi-scenario personalized recommendations. |
Moyu Zhang; Yongxiang Tang; Jinxin Hu; Yu Zhang; |
| 150 | Scaling Sequential Recommendation Models with Transformers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our case, we start from a well-known transformer-based model from the literature and make two crucial modifications. |
Pablo Zivic; Hernan Vazquez; Jorge S\'{a}nchez; |
| 151 | A Generic Behavior-Aware Data Augmentation Framework for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: During the augmentation of samples, it is easy to introduce excessive disturbance or noise, which may mislead the next-item recommendation. To address this limitation, we propose a novel generic framework called multi-behavior data augmentation for sequential recommendation (MBASR). |
Jing Xiao; Weike Pan; Zhong Ming; |
| 152 | CMCLRec: Cross-modal Contrastive Learning for User Cold-start Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, due to the lack of interaction information, new users face challenges when utilizing sequential recommendation models for predictions, which is recognized as the cold-start problem. Recent studies, while addressing this problem within specific structures, often neglect the compatibility with existing sequential recommendation models, making seamless integration into existing models unfeasible.To address this challenge, we propose CMCLRec, a Cross-Modal Contrastive Learning framework for user cold-start RECommendation. |
Xiaolong Xu; Hongsheng Dong; Lianyong Qi; Xuyun Zhang; Haolong Xiang; Xiaoyu Xia; Yanwei Xu; Wanchun Dou; |
| 153 | FineRec: Exploring Fine-grained Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. |
Xiaokun Zhang; Bo Xu; Youlin Wu; Yuan Zhong; Hongfei Lin; Fenglong Ma; |
| 154 | SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Such noise negatively impacts the accuracy of both graph and sequence models, further complicating the modeling process. To address these challenges, we propose a novel framework called Self-Supervised Graph Neural Network (SelfGNN) for sequential recommendation. |
Yuxi Liu; Lianghao Xia; Chao Huang; |
| 155 | EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To capture user preference, transformer models have been widely applied to model sequential user behavior data. |
Zhen Tian; Wayne Xin Zhao; Changwang Zhang; Xin Zhao; Zhongrui Ma; Ji-Rong Wen; |
| 156 | Bootstrap Deep Metric for Seed Expansion in Attributed Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce Bootstrap Deep Metric (BDM), a graph deep metric learning framework for seed expansion problems. |
Chunquan Liang; Yifan Wang; Qiankun Chen; Xinyuan Feng; Luyue Wang; Mei Li; Hongming Zhang; |
| 157 | Grand: A Fast and Accurate Graph Retrieval Framework Via Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to combine the advantage of Ebp in retrieval efficiency with that of Mbp in retrieval accuracy, we propose a novel Graph RetrievAl framework via KNowledge Distillation, namely GRAND. |
Lin Lan; Pinghui Wang; Rui Shi; Tingqing Liu; Juxiang Zeng; Feiyang Sun; Yang Ren; Jing Tao; Xiaohong Guan; |
| 158 | Intent Distribution Based Bipartite Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This makes it difficult to represent the macroscopic structural information and leaves it easily affected by data sparsity and noise. To address this issue, we propose the Intent Distribution based Bipartite graph Representation learning (IDBR) model, which explicitly integrates node intent distribution information into the representation learning process. |
Haojie Li; Wei Wei; Guanfeng Liu; Jinhuan Liu; Feng Jiang; Junwei Du; |
| 159 | TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose TGOnline, an adaptive online meta-learning framework, tackling two key challenges. |
Ruijie Wang; Jingyuan Huang; Yutong Zhang; Jinyang Li; Yufeng Wang; Wanyu Zhao; Shengzhong Liu; Charith Mendis; Tarek Abdelzaher; |
| 160 | A Dual-Embedding Based DQN for Worker Recruitment in Spatial Crowdsourcing with Social Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We prove that the ECM problem is NP-hard and propose a novel worker recruitment method combined with the dual-embedding and Rainbow Deep Q-network (DQN), which is called DQNSelector. |
Yucen Gao; Wei Liu; Jianxiong Guo; Xiaofeng Gao; Guihai Chen; |
| 161 | Scalable Community Search Over Large-scale Graphs Based on Graph Transformer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (3) Existing methods do not adapt well to dynamic graphs, often requiring retraining from scratch. To handle this, we present CSFormer, a scalable CS based on Graph Transformer. |
Yuxiang Wang; Xiaoxuan Gou; Xiaoliang Xu; Yuxia Geng; Xiangyu Ke; Tianxing Wu; Zhiyuan Yu; Runhuai Chen; Xiangying Wu; |
| 162 | Efficient Community Search Based on Relaxed K-Truss Index Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the k-truss based community search algorithms can not meet users’ real-time demands on large graphs. To address the above problems, this paper proposes the relaxed k-truss community search problem for the first time. |
Xiaoqin Xie; Shuangyuan Liu; Jiaqi Zhang; Shuai Han; Wei Wang; Wu Yang; |
| 163 | Untargeted Adversarial Attack on Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore untargeted attacks with the aim of reducing the global performances of KGE methods over a set of unknown test triples and conducting systematic analyses on KGE robustness. |
Tianzhe Zhao; Jiaoyan Chen; Yanchi Ru; Qika Lin; Yuxia Geng; Jun Liu; |
| 164 | Poisoning Decentralized Collaborative Recommender System and Its Countermeasures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although research on such poisoning attacks provides valuable insights into finding security loopholes and corresponding countermeasures, existing attacks mostly focus on FedRecs, and are either inapplicable or ineffective for DecRecs. Compared with FedRecs where the tampered information can be universally distributed to all clients once uploaded to the cloud, each adversary in DecRecs can only communicate with neighbor clients of a small size, confining its impact to a limited range.To fill the gap, we present a novel attack method named Poisoning with Adaptive Malicious Neighbors (PAMN). |
Ruiqi Zheng; Liang Qu; Tong Chen; Kai Zheng; Yuhui Shi; Hongzhi Yin; |
| 165 | Revisit Targeted Model Poisoning on Federated Recommendation: Optimize Via Multi-objective Transport Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the targeted model poisoning attack against FedRec, which aims at effectively attacking the FedRec via uploading poisoned gradients to raise the exposure ratio of a multi-target item set. |
Jiajie Su; Chaochao Chen; Weiming Liu; Zibin Lin; Shuheng Shen; Weiqiang Wang; Xiaolin Zheng; |
| 166 | LoRec: Combating Poisons with Large Language Model for Robust Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose LoRec, an innovative framework that employs LLM-Enhanced Calibration to strengthen the robustness of sequential Recommender systems against poisoning attacks. |
Kaike Zhang; Qi Cao; Yunfan Wu; Fei Sun; Huawei Shen; Xueqi Cheng; |
| 167 | Improving The Accuracy of Locally Differentially Private Community Detection By Order-consistent Data Perturbation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes LDP-Cd, a two-phase community detection framework under local differential privacy. |
Taolin Guo; Shunshun Peng; Zhejian Zhang; Mengmeng Yang; Kwok-Yan Lam; |
| 168 | Unmasking Privacy: A Reproduction and Evaluation Study of Obfuscation-based Perturbation Techniques for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While the framework presents great advantages – low computational requirements, several useful privacy-enhancing parameters – the supporting paper lacks conclusions drawn from empirical evaluation. We address this shortcoming by proposing – in absence of an implementation by the authors – our own implementation of the obfuscation framework. |
Alex Martinez; Mihnea Tufis; Ludovico Boratto; |
| 169 | ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle challenges in implementing Fully-VFR, we propose a Retrieval-enhanced Vertical Federated recommender (ReFer), a groundbreaking initiative that explores retrieval-enhanced machine learning approaches in VFL. |
Wenjie Li; Zhongren Wang; Jinpeng Wang; Shu-Tao Xia; Jile Zhu; Mingjian Chen; Jiangke Fan; Jia Cheng; Jun Lei; |
| 170 | GPT4Rec: Graph Prompt Tuning for Streaming Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods in this area either rely on historical data replay, which is increasingly impractical due to stringent data privacy regulations; or are inability to effectively address the over-stability issue; or depend on model-isolation and expansion strategies, which necessitate extensive model expansion and are hampered by time-consuming updates due to large parameter sets. To tackle these difficulties, we present GPT4Rec, a Graph Prompt Tuning method for streaming Recommendation. |
Peiyan Zhang; Yuchen Yan; Xi Zhang; Liying Kang; Chaozhuo Li; Feiran Huang; Senzhang Wang; Sunghun Kim; |
| 171 | LLaRA: Large Language-Recommendation Assistant Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To combine the complementary strengths of conventional recommenders in capturing behavioral patterns of users and LLMs in encoding world knowledge about items, we introduce Large Language-Recommendation Assistant (LLaRA). |
Jiayi Liao; Sihang Li; Zhengyi Yang; Jiancan Wu; Yancheng Yuan; Xiang Wang; Xiangnan He; |
| 172 | Let Me Do It For You: Towards LLM Empowered Recommendation Via Tool Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing LLM-based RSs suffer from hallucinations, misalignment between the semantic space of items and the behavior space of users, or overly simplistic control strategies (e.g., whether to rank or directly present existing results). To bridge these gap, we introduce ToolRec, a framework for LLM-empowered recommendations via tool learning that uses LLMs as surrogate users, thereby guiding the recommendation process and invoking external tools to generate a recommendation list that aligns closely with users’ nuanced preferences.We formulate the recommendation process as a process aimed at exploring user interests in attribute granularity. |
Yuyue Zhao; Jiancan Wu; Xiang Wang; Wei Tang; Dingxian Wang; Maarten de Rijke; |
| 173 | On Generative Agents in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose Agent4Rec, a user simulator in recommendation, leveraging LLM-empowered generative agents equipped with user profile, memory, and actions modules specifically tailored for the recommender system. |
An Zhang; Yuxin Chen; Leheng Sheng; Xiang Wang; Tat-Seng Chua; |
| 174 | Drop Your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The usage of additional Transformer-based decoders also incurs significant computational costs. In this study, we aim to shed light on this issue by revealing that masked auto-encoder (MAE) pre-training with enhanced decoding significantly improves the term coverage of input tokens in dense representations, compared to vanilla BERT checkpoints. |
Guangyuan Ma; Xing Wu; Zijia Lin; Songlin Hu; |
| 175 | Generative Retrieval As Multi-Vector Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, prior work focuses solely on the retrieval stage without considering the deep interactions within the decoder of generative retrieval. In this paper, we fill this gap by demonstrating that generative retrieval and multi-vector dense retrieval share the same framework for measuring the relevance to a query of a document. |
Shiguang Wu; Wenda Wei; Mengqi Zhang; Zhumin Chen; Jun Ma; Zhaochun Ren; Maarten de Rijke; Pengjie Ren; |
| 176 | I3: Intent-Introspective Retrieval Conditioned on Instructions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent studies indicate that dense retrieval models struggle to perform well on a wide variety of retrieval tasks that lack dedicated training data, as different retrieval tasks often entail distinct search intents. To address this challenge, in this work we leverage instructions to flexibly describe retrieval intents and introduce I3, a unified retrieval system that performs <u>I</u>ntent-<u>I</u>ntrospective retrieval across various tasks, conditioned on Instructions without any task-specific training. |
Kaihang Pan; Juncheng Li; Wenjie Wang; Hao Fei; Hongye Song; Wei Ji; Jun Lin; Xiaozhong Liu; Tat-Seng Chua; Siliang Tang; |
| 177 | Reinforcing Long-Term Performance in Recommender Systems with User-Oriented Exploration Policy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, previous studies often overlook this aspect and apply a uniform exploration strategy to all users, which ultimately hampers long-term user experiences. To tackle these challenges, we propose User-Oriented Exploration Policy (UOEP), a novel approach that enables fine-grained exploration among user groups. |
Changshuo Zhang; Sirui Chen; Xiao Zhang; Sunhao Dai; Weijie Yu; Jun Xu; |
| 178 | Treatment Effect Estimation for User Interest Exploration on Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose an Uplift model-based Recommender (UpliftRec) framework, which regards top-N recommendation as a treatment optimization problem. |
Jiaju Chen; Wang Wenjie; Chongming Gao; Peng Wu; Jianxiong Wei; Qingsong Hua; |
| 179 | Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel decision transformer-based recommendation model, DT4IER, to not only elevate the effectiveness of recommendations but also to achieve a harmonious balance between immediate user engagement and long-term retention. |
Ziru Liu; Shuchang Liu; Zijian Zhang; Qingpeng Cai; Xiangyu Zhao; Kesen Zhao; Lantao Hu; Peng Jiang; Kun Gai; |
| 180 | Disentangling ID and Modality Effects for Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a novel framework DIMO to disentangle the effects of ID and modality in the task. |
Xiaokun Zhang; Bo Xu; Zhaochun Ren; Xiaochen Wang; Hongfei Lin; Fenglong Ma; |
| 181 | Large Language Models Are Learnable Planners for Long-Term Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose a Bi-level Learnable LLM Planner framework, which consists of a set of LLM instances and breaks down the learning process into macro-learning and micro-learning to learn macro-level guidance and micro-level personalized recommendation policies, respectively. |
Wentao Shi; Xiangnan He; Yang Zhang; Chongming Gao; Xinyue Li; Jizhi Zhang; Qifan Wang; Fuli Feng; |
| 182 | On The Evaluation of Machine-Generated Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These qualities, which are desirable—if not required—in many analytic report-writing settings, require rethinking how to build and evaluate systems that exhibit these qualities. To foster new efforts in building these systems, we present an evaluation framework that draws on ideas found in various evaluations. |
James Mayfield; Eugene Yang; Dawn Lawrie; Sean MacAvaney; Paul McNamee; Douglas W. Oard; Luca Soldaini; Ian Soboroff; Orion Weller; Efsun Kayi; Kate Sanders; Marc Mason; Noah Hibbler; |
| 183 | Evaluating Generative Ad Hoc Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, the established evaluation methodology for ranking-based ad hoc retrieval is not suited for the reliable and reproducible evaluation of generated responses. To lay a foundation for developing new evaluation methods for generative retrieval systems, we survey the relevant literature from the fields of information retrieval and natural language processing, identify search tasks and system architectures in generative retrieval, develop a new user model, and study its operationalization. |
Lukas Gienapp; Harrisen Scells; Niklas Deckers; Janek Bevendorff; Shuai Wang; Johannes Kiesel; Shahbaz Syed; Maik Fr\{o}be; Guido Zuccon; Benno Stein; Matthias Hagen; Martin Potthast; |
| 184 | Large Language Models Can Accurately Predict Searcher Preferences Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We discuss an alternative approach. We take careful feedback from real searchers and use this to select a large language model (LLM), and prompt, that agrees with this feedback; the LLM can then produce labels at scale. |
Paul Thomas; Seth Spielman; Nick Craswell; Bhaskar Mitra; |
| 185 | Are Large Language Models Good at Utility Judgments? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: (iv) We propose a k-sampling, listwise approach to reduce the dependency of LLMs on the sequence of input passages, thereby facilitating subsequent answer generation. |
Hengran Zhang; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Yixing Fan; Xueqi Cheng; |
| 186 | Rethinking The Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We use both crowdworkers and large language models (LLMs) as annotators to assess system responses across four aspects: relevance, usefulness, interestingness, and explanation quality. |
Clemencia Siro; Mohammad Aliannejadi; Maarten de Rijke; |
| 187 | A Workbench for Autograding Retrieve/Generate Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This resource paper addresses the challenge of evaluating Information Retrieval (IR) systems in the era of autoregressive Large Language Models (LLMs). |
Laura Dietz; |
| 188 | CIQA: A Coding Inspired Question Answering Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel domain-agnostic model to address the problem by leveraging domain-specific and open-source code libraries. |
Mousa Arraf; Kira Radinsky; |
| 189 | Let Me Show You Step By Step: An Interpretable Graph Routing Network for Knowledge-based Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel interpretable graph routing network (GRN) which explicitly conducts entity routing over a constructed scene knowledge graph step by step for KB-VQA. |
Duokang Wang; Linmei Hu; Rui Hao; Yingxia Shao; Xin Lv; Liqiang Nie; Juanzi Li; |
| 190 | MTMS: Multi-teacher Multi-stage Knowledge Distillation for Reasoning-Based Machine Reading Comprehension Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, prior to our work, there were no small models specifically designed for MRC task with complex reasoning abilities. In light of this, we present a novel multi-teacher multi-stage distillation approach, MTMS. |
Zhuo Zhao; Zhiwen Xie; Guangyou Zhou; Jimmy Xiangji Huang; |
| 191 | Exploring The Trade-Off Within Visual Information for MultiModal Sentence Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a novel method, T(3), which adopts IB to balance the <u>T</u>rade-off between <u>T</u>ask-relevant and <u>T</u>ask-irrelevant visual information through the variational inference framework. |
Minghuan Yuan; Shiyao Cui; Xinghua Zhang; Shicheng Wang; Hongbo Xu; Tingwen Liu; |
| 192 | Flexible and Adaptable Summarization Via Expertise Separation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A proficient summarization model should exhibit both flexibility — the capacity to handle a range of in-domain summarization tasks, and adaptability — the competence to acquire new knowledge and adjust to unseen out-of-domain tasks. Unlike large language models (LLMs) that achieve this through parameter scaling, we propose a more parameter-efficient approach in this study. |
Xiuying Chen; Mingzhe Li; Shen Gao; Xin Cheng; Qingqing Zhu; Rui Yan; Xin Gao; Xiangliang Zhang; |
| 193 | Disentangling Instructive Information from Ranked Multiple Candidates for Multi-Document Scientific Summarization Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Drawing on the insights, we propose a summary candidates fusion framework – Disentangling Instructive information from Ranked candidates (DIR) for MDSS. |
Pancheng Wang; Shasha Li; Dong Li; Kehan Long; Jintao Tang; Ting Wang; |
| 194 | ChroniclingAmericaQA: A Large-scale Question Answering Dataset Based on Historical American Newspaper Pages Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To further contribute to advancing QA and MRC tasks and to overcome the limitation of previous datasets, we introduce ChroniclingAmericaQA, a large-scale temporal QA dataset with 487K question-answer pairs created based on the historical newspaper collection Chronicling America. |
Bhawna Piryani; Jamshid Mozafari; Adam Jatowt; |
| 195 | ArabicaQA: A Comprehensive Dataset for Arabic Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. |
Abdelrahman Abdallah; Mahmoud Kasem; Mahmoud Abdalla; Mohamed Mahmoud; Mohamed Elkasaby; Yasser Elbendary; Adam Jatowt; |
| 196 | TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a framework for the automatic hint generation for factoid questions, employing it to construct TriviaHG, a novel large-scale dataset featuring 160,230 hints corresponding to 16,645 questions from the TriviaQA dataset. |
Jamshid Mozafari; Anubhav Jangra; Adam Jatowt; |
| 197 | Pacer and Runner: Cooperative Learning Framework Between Single- and Cross-Domain Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the issue of negative transfer, our proposed CDSR model estimates the degree of negative transfer of each domain and adaptively assigns it as a weight factor to the prediction loss, to control gradient flows through domains with significant negative transfer. |
Chung Park; Taesan Kim; Hyungjun Yoon; Junui Hong; Yelim Yu; Mincheol Cho; Minsung Choi; Jaegul Choo; |
| 198 | Aiming at The Target: Filter Collaborative Information for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing efforts to tackle this issue primarily focus on designing adaptive representations for overlapped users. Whereas, these methods rely on the learned representations of the model, lacking explicit constraints to filter irrelevant source-domain collaborative information for the target domain, which limits their cross-domain transfer capability.In this paper, we propose a novel Collaborative information regularized User Transformation (CUT) framework to tackle the negative transfer problem by directly filtering users’ collaborative information. |
Hanyu Li; Weizhi Ma; Peijie Sun; Jiayu Li; Cunxiang Yin; Yancheng He; Guoqiang Xu; Min Zhang; Shaoping Ma; |
| 199 | Identifiability of Cross-Domain Recommendation Via Causal Subspace Disentanglement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Hierarchical causal subspace disentanglement approach to explore the Joint IDentifiability of cross-domain joint distribution, termed HJID, to preserve domain-specific behaviors from domain-shared factors. |
Jing Du; Zesheng Ye; Bin Guo; Zhiwen Yu; Lina Yao; |
| 200 | On The Negative Perception of Cross-domain Recommendations and Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Similarly, while single-domain explanations have been shown to improve users’ perceptions of recommendations, there are no comparable studies for the cross-domain case.In this article, we present a between-subject study (N=237) of users’ behavioural intentions and perceptions of book recommendations. |
Denis Kotkov; Alan Medlar; Yang Liu; Dorota Glowacka; |
| 201 | DeCoCDR: Deployable Cloud-Device Collaboration for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Deployment-friendly Cloud-Device Collaboration framework for Cross-Domain Recommendation (DeCoCDR). |
Yu Li; Yi Zhang; Zimu Zhou; Qiang Li; |
| 202 | Mutual Information-based Preference Disentangling and Transferring for Non-overlapped Multi-target Cross-domain Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We hence propose MITrans, a novel mutual information-based (MI-based) preference disentangling and transferring framework to improve recommendations for all domains. |
Zhi Li; Daichi Amagata; Yihong Zhang; Takahiro Hara; Shuichiro Haruta; Kei Yonekawa; Mori Kurokawa; |
| 203 | Multi-Domain Sequential Recommendation Via Domain Space Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In such cases, the history of users in the target domain is limited or not recent, leading the sequential recommender system to capture inaccurate domain-specific sequential preferences. To address this limitation, this paper introduces Multi-Domain Sequential Recommendation via Domain Space Learning (MDSR-DSL). |
Junyoung Hwang; Hyunjun Ju; SeongKu Kang; Sanghwan Jang; Hwanjo Yu; |
| 204 | Capability-aware Prompt Reformulation Learning for Text-to-Image Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our in-depth analysis of these logs reveals that user prompt reformulation is heavily dependent on the individual user’s capability, resulting in significant variance in the quality of reformulation pairs. To effectively use this data for training, we introduce the Capability-aware Prompt Reformulation (CAPR) framework. |
Jingtao Zhan; Qingyao Ai; Yiqun Liu; Jia Chen; Shaoping Ma; |
| 205 | M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training Towards Effective and Efficient Zero-shot Video-text Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present a Recipe for Effective and Efficient zero-shot video-text Retrieval, dubbed M2-RAAP. |
Xingning Dong; Zipeng Feng; Chunluan Zhou; Xuzheng Yu; Ming Yang; Qingpei Guo; |
| 206 | Short Video Ordering Via Position Decoding and Successor Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, accurately reordering videos within a collection based on their content coherence is a vital task that can enhance user experience and presents an intriguing research problem in the field of video narrative reasoning. In this work, we curate a dedicated multimodal dataset for this Short Video Ordering (SVO) task and present the performance of some benchmark methods on the dataset. |
Shiping Ge; Qiang Chen; Zhiwei Jiang; Yafeng Yin; Ziyao Chen; Qing Gu; |
| 207 | CaLa: Complementary Association Learning for Augmenting Comoposed Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we disclose two new relations residing in the triplets by viewing the triplet as a graph node. |
Xintong Jiang; Yaxiong Wang; Mengjian Li; Yujiao Wu; Bingwen Hu; Xueming Qian; |
| 208 | CFIR: Fast and Effective Long-Text To Image Retrieval for Large Corpora Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a two-stage Coarse-to-Fine Index-shared Retrieval (CFIR) framework, designed for fast and effective large-scale long-text to image retrieval. |
Zijun Long; Xuri Ge; Richard McCreadie; Joemon M. Jose; |
| 209 | CaseLink: Inductive Graph Learning for Legal Case Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Thus, in this paper, a CaseLink model based on inductive graph learning is proposed to utilise the intrinsic case connectivity for legal case retrieval, a novel Global Case Graph is incorporated to represent both the case semantic relationship and the case legal charge relationship. |
Yanran Tang; Ruihong Qiu; Hongzhi Yin; Xue Li; Zi Huang; |
| 210 | Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Neither approach provides explicit evidence of judgment consistency for relevance modeling, leading to inaccuracies and a lack of transparency in retrieval. To address this issue, we propose a law-guided method, namely GEAR, within the generative retrieval framework. |
Weicong Qin; Zelin Cao; Weijie Yu; Zihua Si; Sirui Chen; Jun Xu; |
| 211 | Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, in this paper, we propose an Event Grounded Generation (EGG) method for criminal court view generation with cooperative (Large) Language Models, which introduces the fine-grained event information into the generation. |
Linan Yue; Qi Liu; Lili Zhao; Li Wang; Weibo Gao; Yanqing An; |
| 212 | Legal Statute Identification: A Case Study Using State-of-the-Art Datasets and Methods Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we reproduce several LSI models on two popular LSI datasets and study the effect of the above-mentioned challenges. |
Shounak Paul; Rajas Bhatt; Pawan Goyal; Saptarshi Ghosh; |
| 213 | CivilSum: A Dataset for Abstractive Summarization of Indian Court Decisions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These factors also increase the costs of annotating large datasets, which are required to train state-of-the-art summarization systems. To address these challenges, we introduce CivilSum, a collection of 23,350 legal case decisions from the Supreme Court of India and other Indian High Courts paired with human-written summaries. |
Manuj Malik; Zheng Zhao; Marcio Fonseca; Shrisha Rao; Shay B. Cohen; |
| 214 | LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. |
Haitao Li; Yunqiu Shao; Yueyue Wu; Qingyao Ai; Yixiao Ma; Yiqun Liu; |
| 215 | A Learning-to-Rank Formulation of Clustering-Based Approximate Nearest Neighbor Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we make a simple observation: The routing function solves a ranking problem. |
Thomas Vecchiato; Claudio Lucchese; Franco Maria Nardini; Sebastian Bruch; |
| 216 | A Persona-Infused Cross-Task Graph Network for Multimodal Emotion Recognition with Emotion Shift Detection in Conversations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Early ES research fails to distinguish diverse shift patterns and simply introduces whether shifts occur as knowledge into the MERC model without considering the complementary nature of the two tasks. |
Geng Tu; Feng Xiong; Bin Liang; Ruifeng Xu; |
| 217 | A Surprisingly Simple Yet Effective Multi-Query Rewriting Method for Conversational Passage Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose the use of a neural query rewriter to generate multiple queries and show how to integrate those queries in the passage retrieval pipeline efficiently. |
Ivica Kostric; Krisztian Balog; |
| 218 | Analyzing and Mitigating Repetitions in Trip Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (2) During training and decoding, adding perturbations to logits can reduce repetition. Motivated by these observations, we introduce AR-Trip (Anti Repetition for Trip Recommendation), which incorporates a cycle-aware predictor comprising three mechanisms to avoid duplicate Points-of-Interest (POIs) and demonstrates their effectiveness in alleviating repetition. |
Wenzheng Shu; Kangqi Xu; Wenxin Tai; Ting Zhong; Yong Wang; Fan Zhou; |
| 219 | Analyzing Fusion Methods Using The Condorcet Rule Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We use the Condorcet voting rule to theoretically and empirically analyze fusion methods. |
Liron Tyomkin; Oren Kurland; |
| 220 | Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite its importance, existing studies in CRS lack a study about how to measure such behavior discrepancy. To fill this gap, we propose Behavior Alignment, a new evaluation metric to measure how well the recommendation strategies made by a LLM-based CRS are consistent with human recommenders’. |
Dayu Yang; Fumian Chen; Hui Fang; |
| 221 | Behavior Pattern Mining-based Multi-Behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods do not adequately explore the intricate patterns of behavior among users and items. To bridge this gap, we introduce a novel algorithm called Behavior Pattern mining-based Multi-behavior Recommendation (BPMR). |
Haojie Li; Zhiyong Cheng; Xu Yu; Jinhuan Liu; Guanfeng Liu; Junwei Du; |
| 222 | Bi-Objective Negative Sampling for Sensitivity-Aware Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose three novel negative sampling strategies that enable cross-encoders to be trained to satisfy the bi-objective task of SAS. |
Jack McKechnie; Graham McDonald; Craig Macdonald; |
| 223 | Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a conversation-level RAG (ConvRAG) approach, which incorporates fine-grained retrieval augmentation and self-check for conversational question answering (CQA). |
Linhao Ye; Zhikai Lei; Jianghao Yin; Qin Chen; Jie Zhou; Liang He; |
| 224 | BRB-KMeans: Enhancing Binary Data Clustering for Binary Product Quantization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this approach often leads to a degradation in clustering quality, negatively impacting BPQ’s performance. To address these challenges, we introduce Binary-to-Real-and-Back K-Means (BRB-KMeans), a novel method that initially transforms binary data into real-valued vectors, performs k-means clustering on these vectors, and then converts the generated centroids back into binary data. |
Suwon Lee; Sang-Min Choi; |
| 225 | Breaking The Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To break through the efficiency barrier of LLMs, we propose Behavior Aggregated Hierarchical Encoding (BAHE) to enhance the efficiency of LLM-based CTR modeling. |
Binzong Geng; Zhaoxin Huan; Xiaolu Zhang; Yong He; Liang Zhang; Fajie Yuan; Jun Zhou; Linjian Mo; |
| 226 | Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchange Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we follow a two-step approach to investigating the proficiency of LLMs in answering mathematical questions. |
Ankit Satpute; Noah Gie\ss{}ing; Andr\'{e} Greiner-Petter; Moritz Schubotz; Olaf Teschke; Akiko Aizawa; Bela Gipp; |
| 227 | Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A recent study shows that current expansion techniques benefit weaker models but harm stronger rankers. In this paper, we re-examine this conclusion and raise the following question: Can query expansion improve generalization of strong cross-encoder rankers? |
Minghan Li; Honglei Zhuang; Kai Hui; Zhen Qin; Jimmy Lin; Rolf Jagerman; Xuanhui Wang; Michael Bendersky; |
| 228 | Cluster-based Partial Dense Retrieval Fused with Sparse Text Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a cluster-based partial dense retrieval scheme guided by sparse retrieval results to optimize fusion between dense and sparse retrieval at a low space and CPU-time cost while retaining a competitive relevance. |
Yingrui Yang; Parker Carlson; Shanxiu He; Yifan Qiao; Tao Yang; |
| 229 | Combining Large Language Models and Crowdsourcing for Hybrid Human-AI Misinformation Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Research on misinformation detection has primarily focused either on furthering Artificial Intelligence (AI) for automated detection or on studying humans’ ability to deliver an effective crowdsourced solution. |
Xia Zeng; David La Barbera; Kevin Roitero; Arkaitz Zubiaga; Stefano Mizzaro; |
| 230 | Contextualization with SPLADE for High Recall Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we leverage SPLADE, an efficient retrieval model that transforms documents into contextualized sparse vectors, for HRR. |
Eugene Yang; |
| 231 | Convex Feature Embedding for Face and Voice Association Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a straightforward yet effective approach for cross-modal feature embedding, specifically targeting the correlation between facial and voice association. |
Jiwoo Kang; Taewan Kim; Young-ho Park; |
| 232 | Counterfactual Augmentation for Robust Authorship Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the performance of authorship attribution models often degrades significantly when texts are from different domains than the training data. In this work, we propose addressing this issue by adopting a novel causal framework for authorship representation learning. |
Hieu Man; Thien Huu Nguyen; |
| 233 | Cross-reconstructed Augmentation for Dual-target Cross-domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present CrossAug, a novel data augmentation approach to leverage interactions more efficiently in two domains. |
Qingyang Mao; Qi Liu; Zhi Li; Likang Wu; Bing Lv; Zheng Zhang; |
| 234 | Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose an alternative approach that still relies on neural models, but leverages dense representations and relevance feedback to enhance screening prioritisation, without the need for costly model fine-tuning and inference. |
Xinyu Mao; Shengyao Zhuang; Bevan Koopman; Guido Zuccon; |
| 235 | Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data Manipulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach to tackle the data imbalance problem in audio-language retrieval task. |
Yoori Oh; Yoseob Han; Kyogu Lee; |
| 236 | Distillation for Multilingual Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work extends Translate-Distill and propose Multilingual Translate-Distill (MTD) for MLIR. |
Eugene Yang; Dawn Lawrie; James Mayfield; |
| 237 | EASE-DR: Enhanced Sentence Embeddings for Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we split the query and the document in information retrieval into two sets of natural sentences and generate their sentence embeddings with BERT, the most popular pre-trained model. |
Xixi Zhou; Yang Gao; Xin Jie; Xiaoxu Cai; Jiajun Bu; Haishuai Wang; |
| 238 | Enhancing Criminal Case Matching Through Diverse Legal Factors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a two-stage framework named Diverse Legal Factor-enhanced Criminal Case Matching (DLF-CCM). |
Jie Zhao; Ziyu Guan; Wei Zhao; Yue Jiang; |
| 239 | Enhancing Task Performance in Continual Instruction Fine-tuning Through Format Uniformity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel framework that enhances CIF by promoting format uniformity. |
Xiaoyu Tan; Leijun Cheng; Xihe Qiu; Shaojie Shi; Yuan Cheng; Wei Chu; Yinghui Xu; Yuan Qi; |
| 240 | Estimating The Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This is in stark contrast with the general field of LTR where Gradient Boosted Decision Trees (GBDTs) have long been considered the state-of-the-art. In this work, we address this gap by introducing the first stochastic LTR method for GBDTs. |
Jingwei Kang; Maarten de Rijke; Harrie Oosterhuis; |
| 241 | Evaluating Retrieval Quality in Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel evaluation approach, eRAG, where each document in the retrieval list is individually utilized by the large language model within the RAG system. |
Alireza Salemi; Hamed Zamani; |
| 242 | Explainable Uncertainty Attribution for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enhance sequential recommendation performance by leveraging uncertainty information, we introduce Explainable Uncertainty Attribution (ExUA). |
Carles Balsells-Rodas; Fan Yang; Zhishen Huang; Yan Gao; |
| 243 | Fake News Detection Via Multi-scale Semantic Alignment and Cross-modal Attention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, very few studies have addressed this issue in fake news detection. In this paper, we delve deeper into this issue and propose a simple yet effective Multi-scale Semantic Alignment and Cross-modal Attention (MSACA) network. |
Jiandong Wang; Hongguang Zhang; Chun Liu; Xiongjun Yang; |
| 244 | Faster Learned Sparse Retrieval with Block-Max Pruning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces Block-Max Pruning (BMP), an innovative dynamic pruning strategy tailored for indexes arising in learned sparse retrieval environments. |
Antonio Mallia; Torsten Suel; Nicola Tonellotto; |
| 245 | FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose FedUD, which is able to exploit unaligned data, in addition to aligned data, for more accurate federated CTR prediction. |
Wentao Ouyang; Rui Dong; Ri Tao; Xiangzheng Liu; |
| 246 | Fine-Tuning LLaMA for Multi-Stage Text Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we leverage LLMs directly to serve as components in the widely used multi-stage text ranking pipeline. |
Xueguang Ma; Liang Wang; Nan Yang; Furu Wei; Jimmy Lin; |
| 247 | From Text to Context: An Entailment Approach for News Stakeholder Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite existing works focusing on salient entity extraction, coverage variations, and political affiliations through social media data, the automated detection of stakeholder roles within news content remains an underexplored domain. In this paper, we bridge this gap by introducing an effective approach to classify stakeholder types in news articles. |
Alapan Kuila; Sudeshna Sarkar; |
| 248 | General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we implement a Transformer-based user model with customized training objectives and show that the model can produce high-quality user representations across a broad range of evaluation tasks, among which we introduce three new downstream tasks that concern pivotal topics in user research: user safety, engagement and churn. |
Qixiang Fang; Zhihan Zhou; Francesco Barbieri; Yozen Liu; Leonardo Neves; Dong Nguyen; Daniel Oberski; Maarten Bos; Ron Dotsch; |
| 249 | Generalizable Tip-of-the-Tongue Retrieval with LLM Re-ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper studies the generalization capabilities of existing retrieval methods with ToT queries in multiple domains. |
Lu\'{\i}s Borges; Rohan Jha; Jamie Callan; Bruno Martins; |
| 250 | Graph Diffusive Self-Supervised Learning for Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To overcome the aforementioned limitations, we introduce a new Graph Diffusive Self-Supervised Learning (GDSSL) paradigm for social recommendation. |
Jiuqiang Li; Hongjun Wang; |
| 251 | Graph Reasoning Enhanced Language Models for Text-to-SQL Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches typically introduce some useful multi-hop structures manually and then incorporate them into graph neural networks (GNNs) by stacking multiple layers, which (1) ignore the difficult-to-identify but meaningful semantics embedded in the multi-hop reasoning path, and (2) are limited by the expressive capability of GNN to capture long-range dependencies among the heterogeneous graph. To address these shortcomings, we introduce GRL-SQL, a graph reasoning enhanced language model, which innovatively applies structure encoding to capture the dependencies between node pairs, encompassing one-hop, multi-hop and distance information, subsequently enriched through self-attention for enhanced representational power over GNNs. |
Zheng Gong; Ying Sun; |
| 252 | Grasping Both Query Relevance and Essential Content for Query-focused Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods still suffer from missing or redundant information due to the inability to capture and effectively utilize the interrelationship between the query and the source document, as well as between the source document and its generated summary, resulting in the summary being unable to answer the query or containing additional unrequired information. To mitigate this problem, we propose an end-to-end hierarchical two-stage summarization model, that first predicts essential content, and then generates a summary by emphasizing the predicted important sentences while maintaining separate encodings for the query and the source, so that it can comprehend not only the query itself but also the essential information in the source. |
Ye Xiong; Hidetaka Kamigaito; Soichiro Murakami; Peinan Zhang; Hiroya Takamura; Manabu Okumura; |
| 253 | IdmGAE: Importance-Inspired Dynamic Masking for Graph Autoencoders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose an efficient masking strategy termed importance-inspired dynamic masking to explore diverse node sampling. |
Ge Chen; Yulan Hu; Sheng Ouyang; Zhirui Yang; Yong Liu; Cuicui Luo; |
| 254 | Improving In-Context Learning Via Sequentially Selection and Preference Alignment for Few-Shot Aspect-Based Sentiment Analysis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we leverage in-context learning (ICL) paradigm to handle few-shot aspect-based sentiment analysis (ABSA). |
Qianlong Wang; Keyang Ding; Xuan Luo; Ruifeng Xu; |
| 255 | Inferring Climate Change Stances from Multimodal Tweets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper explores a simple but effective strategy combining modality fusion and domain-knowledge enhancing to prepare CLIP-based models with knowledge of climate change stances. |
Nan Bai; Ricardo da Silva Torres; Anna Fensel; Tamara Metze; Art Dewulf; |
| 256 | Information Diffusion Prediction Via Cascade-Retrieved In-context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To date, existing methods either focus on capturing limited contextual information from a single cascade, overlooking the potentially complex dependencies across different cascades, or they are committed to improving model performance by using intricate technologies to extract additional features as supplements to user representations, neglecting the drift of model performance across different platforms. To address these limitations, we propose a novel framework called CARE (CAscade-REtrieved In-Context Learning) inspired by the concept of in-context learning in LLMs. |
Ting Zhong; Jienan Zhang; Zhangtao Cheng; Fan Zhou; Xueqin Chen; |
| 257 | Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Here, we study the problem of bullet point summarization of long Earning Call Transcripts (ECTs) using the recently released ECTSum dataset. |
Subhendu Khatuya; Koushiki Sinha; Niloy Ganguly; Saptarshi Ghosh; Pawan Goyal; |
| 258 | Label Hierarchical Structure-Aware Multi-Label Few-Shot Intent Detection Via Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a label hierarchical structure-aware method for multi-label few-shot intent detection via prompt tuning (LHS). |
Xiaotong Zhang; Xinyi Li; Han Liu; Xinyue Liu; Xianchao Zhang; |
| 259 | Language Fairness in Multilingual Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work proposes a language fairness metric to evaluate whether documents across different languages are fairly ranked through statistical equivalence testing using the Kruskal-Wallis test. |
Eugene Yang; Thomas J\{a}nich; James Mayfield; Dawn Lawrie; |
| 260 | Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, traditional stemming methods, focusing solely on individual terms, overlook the richness of contextual information.Recognizing this gap, in this paper, we investigate the promising idea of using large language models (LLMs) to stem words by lever-aging its capability of context understanding. With this respect, we identify three avenues, each characterised by different trade-offs in terms of computational cost, effectiveness and robustness : (1) use LLMs to stem the vocabulary for a collection, i.e., the set of unique words that appear in the collection (vocabulary stemming), (2) use LLMs to stem each document separately (contextual stemming), and (3) use LLMs to extract from each document entities that should not be stemmed, then use vocabulary stemming to stem the rest of the terms (entity-based contextual stemming). |
Shuai Wang; Shengyao Zhuang; Guido Zuccon; |
| 261 | MACA: Memory-aided Coarse-to-fine Alignment for Text-based Person Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel TBPS framework, named Memory-Aided Coarse-to-fine Alignment (MACA), to learn an accurate and reliable alignment between the two modalities. |
Liangxu Su; Rong Quan; Zhiyuan Qi; Jie Qin; |
| 262 | Masked Graph Transformer for Large-Scale Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here we propose an efficient Masked Graph Transformer, named MGFormer, capable of capturing all-pair interactions among nodes with a linear complexity. |
Huiyuan Chen; Zhe Xu; Chin-Chia Michael Yeh; Vivian Lai; Yan Zheng; Minghua Xu; Hanghang Tong; |
| 263 | Memory-Efficient Deep Recommender Systems Using Approximate Rotary Compositional Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Approximate Rotary Compositional Embedding (ARCE), which intentionally trades off performance to aggressively reduce the size of the embedding tables. |
Dongning Ma; Xun Jiao; |
| 264 | MKV: Mapping Key Semantics Into Vectors for Rumor Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a multimodal rumor detection model (MKV), which maps multimodal key semantics with discrimination into feature vectors for rumor detection. |
Yang Li; Liguang Liu; Jiacai Guo; Lap-Kei Lee; Fu Lee Wang; Zhenguo Yang; |
| 265 | Modeling Domains As Distributions with Uncertainty for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, previous efforts frequently lacked a comprehensive investigation of the entire domain distributions. This paper proposes a novel framework entitled Wasserstein Cross-Domain Recommendation (WCDR) that captures uncertainty in Wasserstein space to address above challenges. |
Xianghui Zhu; Mengqun Jin; Hengyu Zhang; Chang Meng; Daoxin Zhang; Xiu Li; |
| 266 | Modeling Scholarly Collaboration and Temporal Dynamics in Citation Networks for Impact Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methodologies face certain challenges, including latent factors affecting citation behaviors and dynamic intrinsic of citation networks. To address these challenges, this study introduces a novel framework named CoDy (modeling scholarly Collaboration and temporal Dynamics in citation networks for impact prediction). |
Pengwei Yan; Yangyang Kang; Zhuoren Jiang; Kaisong Song; Tianqianjin Lin; Changlong Sun; Xiaozhong Liu; |
| 267 | MoME: Mixture-of-Masked-Experts for Efficient Multi-Task Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework called Mixture-of-Masked-Experts (MoME) to address the challenges. |
Jiahui Xu; Lu Sun; Dengji Zhao; |
| 268 | Multi-intent-aware Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This limitation leads to significant performance drops, especially for longer sessions. To address this issue, we propose a novel SBR model, called Multi-intent-aware Session-based Recommendation Model (MiaSRec). |
Minjin Choi; Hye-young Kim; Hyunsouk Cho; Jongwuk Lee; |
| 269 | Multi-Layer Ranking with Large Language Models for News Source Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To seek reliable information sources for news events, we introduce a novel task of expert recommendation, which aims to identify trustworthy sources based on their previously quoted statements. |
Wenjia Zhang; Lin Gui; Rob Procter; Yulan He; |
| 270 | Multi-view Mixed Attention for Contrastive Learning on Hypergraphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Multi-view Mixed Attention for Contrastive Learning (MMACL) to address the aforementioned problem. |
Jongsoo Lee; Dong-Kyu Chae; |
| 271 | Negative As Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we point out that the traditional optimization of InfoNCE in GCL restricts the cross-domain pairs only to be negative samples, which inevitably enlarges the distribution gap between different domains. |
Zixu Wang; Bingbing Xu; Yige Yuan; Huawei Shen; Xueqi Cheng; |
| 272 | Neural Click Models for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models. |
Mikhail Shirokikh; Ilya Shenbin; Anton Alekseev; Anna Volodkevich; Alexey Vasilev; Andrey V. Savchenko; Sergey Nikolenko; |
| 273 | Old IR Methods Meet RAG Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Retrieval augmented generation (RAG) is an important approach to provide large language models (LLMs) with context pertaining to the text generation task: given a prompt, passages are retrieved from external corpora to ground the generation with more relevant and/or fresher data. |
Oz Huly; Idan Pogrebinsky; David Carmel; Oren Kurland; Yoelle Maarek; |
| 274 | On Backbones and Training Regimes for Dense Retrieval in African Languages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The optimal choice of a backbone model for a given retrieval task is dependent on the target retrieval domain as well as the pre-training domain of available language models and their generalization capabilities, the availability of relevance judgements, etc. In this work, we study the impact of these factors on retrieval effectiveness for African languages using three multilingual benchmark datasets: Mr. TyDi, MIRACL, and the newly released CIRAL dataset. |
Akintunde Oladipo; Mofetoluwa Adeyemi; Jimmy Lin; |
| 275 | PAG-LLM: Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that LLMs like LLaMa can achieve high performance on intent classification tasks with large number of classes but still make classification errors and worse, generate out-of-vocabulary intent labels. To address these critical issues, we introduce Paraphrase and AGgregate (PAG)-LLM approach wherein an LLM generates multiple paraphrases of the input query (parallel queries), performs intent classification for the original query and each paraphrase, and at the end aggregate all the predicted intent labels based on their confidence scores. |
Vikas Yadav; Zheng Tang; Vijay Srinivasan; |
| 276 | PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: PLAID, an efficient implementation of the ColBERT late interaction bi-encoder using pretrained language models for ranking, consistently achieves state-of-the-art performance in monolingual, cross-language, and multilingual retrieval. |
Dawn Lawrie; Efsun Kayi; Eugene Yang; James Mayfield; Douglas W. Oard; |
| 277 | Predicting Micro-video Popularity Via Multi-modal Retrieval Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present MMRA, a multi-modal retrieval-augmented popularity prediction model that enhances prediction accuracy using relevant retrieved information. |
Ting Zhong; Jian Lang; Yifan Zhang; Zhangtao Cheng; Kunpeng Zhang; Fan Zhou; |
| 278 | Prediction of The Realisation of An Information Need: An EEG Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As such, within this study, we explore the ability to predict the realisation of IN within EEG data across 14 participants whilst partaking in a Question-Answering (Q/A) task. |
Niall McGuire; Yashar Moshfeghi; |
| 279 | PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this research, we propose PromptLink, a novel biomedical concept linking framework that leverages LLMs. |
Yuzhang Xie; Jiaying Lu; Joyce Ho; Fadi Nahab; Xiao Hu; Carl Yang; |
| 280 | R-ODE: Ricci Curvature Tells When You Will Be Informed Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such limitation motivates us to pose the problem of the time-aware personalized information diffusion prediction for the first time, telling the time when the target user will be informed. In this paper, we address this problem from a fresh geometric perspective of Ricci curvature, and propose a novel Ricci-curvature regulated Ordinary Differential Equation (R-ODE). |
Li Sun; Jingbin Hu; Mengjie Li; Hao Peng; |
| 281 | ReCODE: Modeling Repeat Consumption with Neural ODE Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Drawing inspiration from the flexibility of neural ordinary differential equations (ODE) in capturing the dynamics of complex systems, we propose ReCODE, a novel model-agnostic framework that utilizes neural ODE to model repeat consumption. |
Sunhao Dai; Changle Qu; Sirui Chen; Xiao Zhang; Jun Xu; |
| 282 | RLStop: A Reinforcement Learning Stopping Method for TAR Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present RLStop, a novel Technology Assisted Review (TAR) stopping rule based on reinforcement learning that helps minimise the number of documents that need to be manually reviewed within TAR applications. |
Reem Bin-Hezam; Mark Stevenson; |
| 283 | SCM4SR: Structural Causal Model-based Data Augmentation for Robust Session-based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Many deep neural networks (DNN) based SR have been proposed in the literature, however, they experience performance declines in practice due to inherent biases (e.g., popularity bias) present in training data. To alleviate this, we propose an underlying neural-network (NN) based Structural Causal Model (SCM) which comprises an evolving user behavior (simulator) and recommendation model. |
Muskan Gupta; Priyanka Gupta; Jyoti Narwariya; Lovekesh Vig; Gautam Shroff; |
| 284 | Searching for Physical Documents in Archival Repositories Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper seeks to change that, using a textual-edge graph neural network to learn relations between items from available metadata and from any content that has been digitized. |
Tokinori Suzuki; Douglas W. Oard; Emi Ishita; Yoichi Tomiura; |
| 285 | Self-Explainable Next POI Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such limitations hinder reliability and applicability in risk-sensitive scenarios. Inspired by the information theory, we propose a self-explainable framework with an ante-hoc view called M~for next POI recommendation aimed at overcoming these limitations. |
Kai Yang; Yi Yang; Qiang Gao; Ting Zhong; Yong Wang; Fan Zhou; |
| 286 | Self-Referential Review: Exploring The Impact of Self-Reference Effect in Review Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To validate the efficacy of self-referential reviews, we conducted a user study focusing on online reviews with thirty-four participants. The contributions of our paper are centered around self-referential reviews, highlighting (1) the creation of these reviews using our new prototype, Self-Referential ReviewMaker, (2) their effectiveness in enhancing review helpfulness through the self-reference effect, and (3) the identification of additional factors influencing the self-reference effect with further discussion on enhancing user-focused review systems. |
Kyusik Kim; Hyungwoo Song; Bongwon Suh; |
| 287 | SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work presents a pioneering study on the KG set retrieval problem. |
Zihao Li; Yuyi Ao; Jingrui He; |
| 288 | SPLATE: Sparse Late Interaction Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Efficient late interaction retrieval is based on an optimized multi-step strategy, where an approximate search first identifies a set of candidate documents to re-rank exactly. In this work, we introduce SPLATE, a simple and lightweight adaptation of the ColBERTv2 model which learns an MLM adapter”, mapping its frozen token embeddings to a sparse vocabulary space with a partially learned SPLADE module. |
Thibault Formal; St\'{e}phane Clinchant; Herv\'{e} D\'{e}jean; Carlos Lassance; |
| 289 | Stochastic RAG: End-to-End Retrieval-Augmented Generation Through Expected Utility Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces Stochastic RAG–a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of marginalization and document independence, made in most prior work. |
Hamed Zamani; Michael Bendersky; |
| 290 | Synthetic Test Collections for Retrieval Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we comprehensively investigate whether it is possible to use LLMs to construct fully synthetic test collections by generating not only synthetic judgments but also synthetic queries. |
Hossein A. Rahmani; Nick Craswell; Emine Yilmaz; Bhaskar Mitra; Daniel Campos; |
| 291 | The Surprising Effectiveness of Rankers Trained on Expanded Queries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we improve the ranking performance of hard or difficult queries while maintaining the performance of other queries. |
Abhijit Anand; Venktesh V; Vinay Setty; Avishek Anand; |
| 292 | Timeline Summarization in The Era of LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We employed three different approaches: chunking, knowledge graph-based summarization, and TimeRanker. |
Daivik Sojitra; Raghav Jain; Sriparna Saha; Adam Jatowt; Manish Gupta; |
| 293 | TouchUp-G: Improving Feature Representation Through Graph-Centric Finetuning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we seek to improve the node features obtained from a PM for graph tasks and introduce TouchUp-G, a Detect \& Correct approach for refining node features extracted from PMs. |
Jing Zhu; Xiang Song; Vassilis Ioannidis; Danai Koutra; Christos Faloutsos; |
| 294 | Towards Ethical Item Ranking: A Paradigm Shift from User-Centric to Item-Centric Approaches Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current approaches, particularly those revolving around user-centric reputation scoring, raise ethical concerns associated with scoring individuals. To counter such issues, in this paper, we introduce a novel item ranking system approach that strategically transitions its emphasis from scoring users to calculating item rankings relying exclusively on items’ ratings information, to achieve the same objective. |
Guilherme Ramos; Mirko Marras; Ludovico Boratto; |
| 295 | Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose Turbo-CF, a GF-based CF method that is both training-free and matrix decomposition-free. |
Jin-Duk Park; Yong-Min Shin; Won-Yong Shin; |
| 296 | Unbiased Validation of Technology-Assisted Review for EDiscovery Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although it is well established that recall estimates are valid only when based on independent relevance assessments, and useful only to compare the relative effectiveness of competing methods, these conditions are seldom met when validating eDiscovery efforts in litigation. We present two unbiased validation strategies that embed blind relevance assessments into a technology-assisted review (TAR) process, so as to compare its recall to that which would have been achieved by exhaustive manual review. |
Gordon V Cormack; Maura R Grossman; Andrew Harbison; Tom O’Halloran; Bronagh McManus; |
| 297 | Unifying Graph Retrieval and Prompt Tuning for Graph-Grounded Text Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose to take an alternative perspective instead, viewing graph as the context of texts, as enlightened by retrieval augmented generation. |
Le Dai; Yu Yin; Enhong Chen; Hui Xiong; |
| 298 | USimAgent: Large Language Models for Simulating Search Users Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a LLM-based user search behavior simulator, USimAgent. |
Erhan Zhang; Xingzhu Wang; Peiyuan Gong; Yankai Lin; Jiaxin Mao; |
| 299 | Using Large Language Models for Math Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large language models, such as Orca-2, have demonstrated notable problem-solving abilities in mathematics. |
Behrooz Mansouri; Reihaneh Maarefdoust; |
| 300 | Weighted KL-Divergence for Document Ranking Model Refinement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper contrastively reweights KL divergence terms to prioritize the alignment between a student and a teacher model for proper separation of positive and negative documents. This paper analyzes and evaluates the proposed loss function on the MS MARCO and BEIR datasets to demonstrate its effectiveness in improving the relevance of tested student models. |
Yingrui Yang; Yifan Qiao; Shanxiu He; Tao Yang; |
| 301 | What Do Users Really Ask Large Language Models? An Initial Log Analysis of Google Bard Interactions in The Wild Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show that LLMs can support users in tasks beyond the three main types based on user intent: informational, navigational, and transactional. |
Johanne R. Trippas; Sara Fahad Dawood Al Lawati; Joel Mackenzie; Luke Gallagher; |
| 302 | A Question-Answering Assistant Over Personal Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on a fine-grained schema customized for PKG, the PKGQA system in this paper comprises Symbolic Semantic Parsing, Frequently Asked Question (FAQ) Semantic Matching, and Neural Semantic Parsing modules, which are designed to take into account both accuracy and efficiency. |
Lingyuan Liu; Huifang Du; Xiaolian Zhang; Mengying Guo; Haofen Wang; Meng Wang; |
| 303 | An Integrated Data Processing Framework for Pretraining Foundation Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Lacking a unified data processing framework, this process is repetitive and cumbersome. To mitigate this issue, we propose a data processing framework that integrates a Processing Module which consists of a series of operators at different granularity levels, and an Analyzing Module which supports probing and evaluation of the refined data. |
Yiding Sun; Feng Wang; Yutao Zhu; Wayne Xin Zhao; Jiaxin Mao; |
| 304 | CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce CLIP-Branches, a novel text-image search engine built upon the CLIP architecture. |
Christian L\{u}lf; Denis Mayr Lima Martins; Marcos Antonio Vaz Salles; Yongluan Zhou; Fabian Gieseke; |
| 305 | ConvLogRecaller: Real-Time Conversational Lifelog Recaller Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a conversational information recall system, ConvLogRecaller, which proactively supports real-time memory recall assistance during online conversations. |
Yuan-Chi Lee; An-Zi Yen; Hen-Hsen Huang; Hsin-Hsi Chen; |
| 306 | CoSearchAgent: A Lightweight Collaborative Search Agent with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In recent years, large language models (LLMs) have been demonstrated to interact naturally with users and achieve complex information-seeking tasks through LLM-based agents. Hence, to better support the research in collaborative search, in this demo, we propose CoSearchAgent, a lightweight collaborative search agent powered by LLMs. |
Peiyuan Gong; Jiamian Li; Jiaxin Mao; |
| 307 | Detecting and Explaining Emotions in Video Advertisements Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework for detecting and, most importantly, explaining emotions in video advertisements. |
Joachim Vanneste; Manisha Verma; Debasis Ganguly; |
| 308 | Embark on DenseQuest: A System for Selecting The Best Dense Retriever for A Custom Collection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. |
Ekaterina Khramtsova; Teerapong Leelanupab; Shengyao Zhuang; Mahsa Baktashmotlagh; Guido Zuccon; |
| 309 | FactCheck Editor: Multilingual Text Editor with End-to-End Fact-checking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce ‘FactCheck Editor’, an advanced text editor designed to automate fact-checking and correct factual inaccuracies. |
Vinay Setty; |
| 310 | Img2Loc: Revisiting Image Geolocalization Using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, classification-based approaches are limited by the cell size and cannot yield precise predictions, while retrieval-based systems usually suffer from poor search quality and inadequate coverage of the global landscape at varied scale and aggregation levels. To overcome these drawbacks, we present Img2Loc, a novel system that redefines image geolocalization as a text generation task. |
Zhongliang Zhou; Jielu Zhang; Zihan Guan; Mengxuan Hu; Ni Lao; Lan Mu; Sheng Li; Gengchen Mai; |
| 311 | JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing state-of-the-art(SOTA) models face challenges due to the unique attributes of our knowledge graph, including directed and undirected relationships, attributed nodes, and minimal annotated competitor connections. To address these challenges, we propose a novel graph embedding model, JPEC(<u>J</u>PMorgan <u>P</u>roximity <u>E</u>mbedding for <u>C</u>ompetitor Detection), which utilizes graph neural network to learn from both first-order and second-order node proximity together with vital features for competitor retrieval. |
Wanying Ding; Manoj Cherukumalli; Santosh Chikoti; Vinay K. Chaudhri; |
| 312 | MACRec: A Multi-Agent Collaboration Framework for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce MACRec, a novel framework designed to enhance recommendation systems through multi-agent collaboration. |
Zhefan Wang; Yuanqing Yu; Wendi Zheng; Weizhi Ma; Min Zhang; |
| 313 | MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To address the pressing need for client-side dense retrieval, we introduce MeMemo, the first open-source JavaScript toolkit that adapts the state-of-the-art approximate nearest neighbor search technique HNSW to browser environments. |
Zijie J. Wang; Duen Horng Chau; |
| 314 | ModelGalaxy: A Versatile Model Retrieval Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One of the key issues to realizing model reuse resides in efficiently and accurately finding the target models that meet user needs from a model repository. |
Wenling Zhang; Yixiao Li; Zhaotian Li; Hailong Sun; Xiang Gao; Xudong Liu; |
| 315 | RAG-Ex: A Generic Framework for Explaining Retrieval Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce RAG-Ex, a model- and language-agnostic explanation framework that presents approximate explanations to the users revealing why the LLMs possibly generated a piece of text as a response, given the user input. |
Viju Sudhi; Sinchana Ramakanth Bhat; Max Rudat; Roman Teucher; |
| 316 | ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We demonstrate the effectiveness of our tool via a https://www.youtube.com/watch?v=Agl7ugyu1N4 and propose novel task-specific evaluation metrics to control for alignment and hallucination. |
Saurabh Bhausaheb Zinjad; Amrita Bhattacharjee; Amey Bhilegaonkar; Huan Liu; |
| 317 | Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We thus introduce a novel technology RA-Rec, a Retrieval-Augmented, LLM-driven dialogue state tracking system for ConvRec, showcased with a video, open source GitHub repository, and interactive Google Colab notebook. |
Sara Kemper; Justin Cui; Kai Dicarlantonio; Kathy Lin; Danjie Tang; Anton Korikov; Scott Sanner; |
| 318 | ScholarNodes: Applying Content-based Filtering to Recommend Interdisciplinary Communities Within Scholarly Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explore an information retrieval method that integrates both partition-based and similarity-based network analysis to identify and recommend communities within content-based datasets. |
Md Asaduzzaman Noor; Jason A. Clark; John W. Sheppard; |
| 319 | Shadowfax: Harnessing Textual Knowledge Base Population Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike other existing tools, it relies on a unified machine learning model to extract relevant information from unstructured text, enabling operational agents to gain a quick overview. |
Maxime Prieur; C\'{e}dric Du Mouza; Guillaume Gadek; Bruno Grilheres; |
| 320 | SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we present SynDy, a framework for <u>Syn</u>thetic <u>Dy</u>namic Dataset Generation to leverage the capabilities of the largest frontier Large Language Models (LLMs) to train local, specialized language models. |
Michael Shliselberg; Ashkan Kazemi; Scott A. Hale; Shiri Dori-Hacohen; |
| 321 | TextData: Save What You Know and Find What You Don’t Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this demonstration, we present TextData, a novel online system that enables users to both save what they know and find what they don’t. |
Kevin Ros; Kedar Takwane; Ashwin Patil; Rakshana Jayaprakash; ChengXiang Zhai; |
| 322 | Towards Robust QA Evaluation Via Open LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their remarkable capabilities, proprietary LLMs are costly and subject to internal changes that can affect their output, which inhibits the reproducibility of their results and limits the widespread adoption of LLM-based evaluation. In this demo, we aim to use publicly available LLMs for standardizing LLM-based QA evaluation. |
Ehsan Kamalloo; Shivani Upadhyay; Jimmy Lin; |
| 323 | Truth-O-Meter: Handling Multiple Inconsistent Sources Repairing LLM Hallucinations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: NLP and reasoning techniques such as Abstract Meaning Representation and syntactic alignment are applied to match hallucinating sentences with truthful ones. |
Boris Galitsky; Anton Chernyavskiy; Dmitry Ilvovsky; |
| 324 | UnKR: A Python Library for Uncertain Knowledge Graph Reasoning By Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we release unKR, the first open-source python library for uncertain Knowledge graph (UKG) Reasoning by representation learning. |
Jingting Wang; Tianxing Wu; Shilin Chen; Yunchang Liu; Shutong Zhu; Wei Li; Jingyi Xu; Guilin Qi; |
| 325 | Ask Me Anything: How Comcast Uses LLMs to Assist Agents in Real Time Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: They need to accurately understand the customer’s question or concern, identify a solution that is acceptable yet feasible (and within the company’s policy), all while handling multiple conversations at once.In this work, we introduce Ask Me Anything (AMA) as an add-on feature to an agent-facing customer service interface. |
Scott Rome; Tianwen Chen; Raphael Tang; Luwei Zhou; Ferhan Ture; |
| 326 | A Field Guide to Automatic Evaluation of LLM-Generated Summaries Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Evaluating LLM-generated summaries is a complex and fast-evolving area, and we propose strategies for applying evaluation methods to avoid common pitfalls. |
Tempest A. van Schaik; Brittany Pugh; |
| 327 | Synthetic Query Generation Using Large Language Models for Virtual Assistants Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we provide a preliminary exploration of the use of Large Language Models (LLMs) to generate synthetic queries that are complementary to template-based methods. |
Sonal Sannigrahi; Thiago Fraga-Silva; Youssef Oualil; Christophe Van Gysel; |
| 328 | Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking Over Larger Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore the challenges associated with establishing an end-to-end fact-checking pipeline in a real-world context, covering over 90 languages. |
Vinay Setty; |
| 329 | LLMGR: Large Language Model-based Generative Retrieval in Alipay Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the challenges, we propose a Large Language Model-based Generative Retrieval (LLMGR) approach for retrieving mini-app candidates. |
Chen Wei; Yixin Ji; Zeyuan Chen; Jia Xu; Zhongyi Liu; |
| 330 | Misinformation Mitigation Praxis: Lessons Learned and Future Directions from Co·Insights Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Co·Insights’ unique cross-sectoral, cross-disciplinary collaboration is a convergence of information retrieval, computational social science, and ethnographic inquiry with a unique platform that enables community organizations, fact-checkers, and academics to work together to respond effectively to harmful content targeting communities. In this SIRIP talk designed for a technical audience, we will share lessons learned from the first 2.5 years of Co·Insights, and how we are bridging the academic–praxis divide by integrating state-of-the-art research developments into large-scale deployed systems. |
Scott A. Hale; Kiran Garimella; Shiri Dori-Hacohen; |
| 331 | Enhancing Baidu Multimodal Advertisement with Chinese Text-to-Image Generation Via Bilingual Alignment and Caption Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a streamlined generation framework for search ad image creatives. |
Kang Zhao; Xinyu Zhao; Zhipeng Jin; Yi Yang; Wen Tao; Cong Han; Shuanglong Li; Lin Liu; |
| 332 | Relevance Feedback Method For Patent Searching Using Vector Subspaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a relevance feedback method based on computing the affine vector subspace spanned by the relevant document vectors. |
Sebastian Bj\{o}rkqvist; |
| 333 | A Study on Unsupervised Question and Answer Generation for Legal Information Retrieval and Precedents Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Legal documents are extensive, and we posit that generating questions about them and detecting the answers provided by these documents help the Legal Research journey. This paper presents a pipeline that relates Legal Questions with documents answering them. |
Johny Moreira; Altigran da Silva; Edleno de Moura; Leandro Marinho; |
| 334 | Homogeneous-listing-augmented Self-supervised Multimodal Product Title Refinement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response to the two challenges, we present a self-supervised multimodal framework (HLATR) for title refinement that comprises two key modules: (1) a perturbated sample generator that constructs training data by systematically mining homogeneous listing information and (2) a title refinement network that effectively harnesses visual information to refine the original titles. |
Jiaqi Deng; Kaize Shi; Huan Huo; Dingxian Wang; Guandong Xu; |
| 335 | Optimizing E-commerce Search: Toward A Generalizable and Rank-Consistent Pre-Ranking Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Contrasting with existing industry works, we propose a novel method: a Generalizable and RAnk-ConsistEnt Pre-Ranking Model (GRACE), which achieves: 1) Ranking consistency by introducing multiple binary classification tasks that predict whether a product is within the top-k results as estimated by the ranking model, which facilitates the addition of learning objectives on common point-wise ranking models; 2) Generalizability through contrastive learning of representation for all products by pre-training on a subset of ranking product embeddings; 3) Ease of implementation in feature construction and online deployment. |
Enqiang Xu; Yiming Qiu; Junyang Bai; Ping Zhang; Dadong Miao; Songlin Wang; Guoyu Tang; Lin Liu; MingMing Li; |
| 336 | A Large-scale Offer Alignment Model for Partitioning Filtering and Matching Product Offers Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work aims to build an offer alignment system that can efficiently be used in large-scale offer data. |
Wenyu Huang; Andr\'{e} Melo; Jeff Z. Pan; |
| 337 | ECAT: A Entire Space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, few of them have simultaneously considered the adaptability of both sample and representation continual transfer setting to the target task. To overcome the above issue, we propose a Entire space Continual and Adaptive Transfer learning framework called ECAT which includes two core components: First, as for sample transfer, we propose a two-stage method that realizes a coarse-to-fine process. |
Chaoqun Hou; Yuanhang Zhou; Yi Cao; Tong Liu; |
| 338 | A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These issues can result in the suboptimal performance of multi-scenario models in handling both S&R scenarios. To address these issues, we propose an effective and universal framework for Unified Search and Recommendation (USR), designed with S&R Views User Interest Extractor Layer (IE) and S&R Views Feature Generator Layer (FG) to separately generate user interests and scenario-agnostic feature representations for S&R. |
Jinhan Liu; Qiyu Chen; Junjie Xu; Junjie Li; Baoli Li; Sulong Xu; |
| 339 | A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conversely, methods designed to promote diversity might compromise the precision of the results, failing to satisfy the users’ requirements for accuracy. To alleviate the above problems, this paper proposes a Preference-oriented Diversity Model Based on Mutual-information (PODM-MI), which consider both accuracy and diversity in the re-ranking process. |
Huimu Wang; Mingming Li; Dadong Miao; Songlin Wang; Guoyu Tang; Lin Liu; Sulong Xu; Jinghe Hu; |
| 340 | Reflections on The Coding Ability of LLMs for Analyzing Market Research Surveys Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present the first systematic study of applying large language models (in our case, GPT-3.5 and GPT-4) for the automatic coding (multi-class classification) problem in market research. |
Shi Zong; Santosh Kolagati; Amit Chaudhary; Josh Seltzer; Jimmy Lin; |
| 341 | Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). |
Zhentao Xu; Mark Jerome Cruz; Matthew Guevara; Tie Wang; Manasi Deshpande; Xiaofeng Wang; Zheng Li; |
| 342 | LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Considering the diverse strengths and weaknesses of LLMs, it becomes necessary to develop an ensemble method that leverages their complementary potentials.In this paper, we propose a novel algorithm called LLM-ensemble to ensemble different LLMs’ outputs for attribute value extraction. |
Chenhao Fang; Xiaohan Li; Zezhong Fan; Jianpeng Xu; Kaushiki Nag; Evren Korpeoglu; Sushant Kumar; Kannan Achan; |
| 343 | Interest Clock: Time Perception in Real-Time Streaming Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an effective and universal method Interest Clock to perceive time information in recommendation systems. |
Yongchun Zhu; Jingwu Chen; Ling Chen; Yitan Li; Feng Zhang; Zuotao Liu; |
| 344 | GATS: Generative Audience Targeting System for Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents GATS (<u>G</u>enerative <u>A</u>udience <u>T</u>argeting <u>S</u> ystem for Online Advertising), a new framework using large language models (LLMs) to improve audience targeting in online advertising. |
Cong Jiang; Zhongde Chen; Bo Zhang; Yankun Ren; Xin Dong; Lei Cheng; Xinxing Yang; Longfei Li; Jun Zhou; Linjian Mo; |
| 345 | Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we describe a simulation methodology used to augment (and reduce) the use of live experiments. |
Chih-Wei Hsu; Martin Mladenov; Ofer Meshi; James Pine; Hubert Pham; Shane Li; Xujian Liang; Anton Polishko; Li Yang; Ben Scheetz; Craig Boutilier; |
| 346 | Improving Embedding-Based Retrieval in Friend Recommendation with ANN Query Expansion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address theses issues by introducing a simple query expansion method in ANN, called FriendSeedSelection, where for each node query, we construct a set of 1-hop embeddings and run ANN search. |
Pau Perng-Hwa Kung; Zihao Fan; Tong Zhao; Yozen Liu; Zhixin Lai; Jiahui Shi; Yan Wu; Jun Yu; Neil Shah; Ganesh Venkataraman; |
| 347 | Monitoring The Evolution of Behavioural Embeddings in Social Media Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We investigate how embeddings change with subsequent updates, explore the relationship between embeddings and popularity bias, and highlight their impact on user engagement metrics. |
Srijan Saket; Olivier Jeunen; Md. Danish Kalim; |
| 348 | Striking The Right Chord: A Comprehensive Approach to Amazon Music Search Spell Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we build a multi-stage framework for spell correction solution for music, media and named entity heavy search engines. |
Siddharth Sharma; Shiyun Yang; Ajinkya Walimbe; Tarun Sharma; Joaquin Delgado; |
| 349 | A Semantic Search Engine for Helping Patients Find Doctors and Locations in A Large Healthcare Organization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces and defines FDL as an important healthcare industry-specific problem in IR. |
Mayank Kejriwal; Hamid Haidarian; Min-Hsueh Chiu; Andy Xiang; Deep Shrestha; Faizan Javed; |
| 350 | Clinical Trial Retrieval Via Multi-grained Similarity Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, it is of great interest to develop an automatic retrieval algorithm to select similar studies by giving new study information. To achieve this goal, we propose a novel group-based trial similarity learning network named GTSLNet, consisting of two kinds of similarity learning modules. |
Junyu Luo; Cheng Qian; Lucas Glass; Fenglong Ma; |
| 351 | Embedding Based Deduplication in E-commerce AutoComplete Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While the existing literature has made significant progress in query similarity learning for e-commerce applications, the specific challenge of query deduplication has received less attention. To address this issue, this paper presents a new industry-scale framework for QAC deduplication at Coupang, utilizing diverse data augmentation techniques to enhance deduplication accuracy effectively. |
Shaodan Zhai; Yuwei Chen; Yixue Li; |
| 352 | Question Suggestion for Conversational Shopping Assistants Using Product Metadata Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we emphasize the importance of providing customers a fast, easy to use, and natural way to interact with conversational shopping assistants. |
Nikhita Vedula; Oleg Rokhlenko; Shervin Malmasi; |
| 353 | SLH-BIA: Short-Long Hawkes Process for Buy It Again Recommendations at Scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we build a short (S) and long (L) term Hawkes (H) process for each item and use it to obtain BIA recommendations for each customer. |
Rankyung Park; Amit Pande; David Relyea; Pushkar Chennu; Prathyusha Kanmanth Reddy; |
| 354 | Graph-Based Audience Expansion Model for Marketing Campaigns Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The major challenges include a heterogeneous user base, intricate marketing campaigns, constraints imposed by sparsity, and limited seed users, which lead to overfitting. In this context, we propose a novel solution named AudienceLinkNet, specifically designed to address the challenges associated with audience expansion in the context of Rakuten’s diverse services and its clients. |
Md Mostafizur Rahman; Daisuke Kikuta; Yu Hirate; Toyotaro Suzumura; |
| 355 | ScienceDirect Topic Pages: A Knowledge Base of Scientific Concepts Across Various Science Domains Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To build TPs, we utilize various information retrieval methods across our product. |
Artemis Capari; Hosein Azarbonyad; Georgios Tsatsaronis; Zubair Afzal; Judson Dunham; |
| 356 | Are Embeddings Enough? SIRIP Panel on The Future of Embeddings in Industry IR Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: The IR community as a whole is considering whether search and recommendations can move entirely to embedding-based technologies. This SIRIP panel discusses the future of … |
Jon Degenhardt; Tracy Holloway King; |
| 357 | Empowering Large Language Models: Tool Learning for Real-World Interaction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This half-day tutorial provides basic concepts of this field and an overview of recent advancements with several applications. In specific, we start with some foundational components and architecture of tool learning (i.e., cognitive tool and physical tool), and then we categorize existing studies in this field into tool-augmented learning and tool-oriented learning, and introduce various learning methods to empower LLMs this kind of capability. |
Hongru Wang; Yujia Qin; Yankai Lin; Jeff Z. Pan; Kam-Fai Wong; |
| 358 | High Recall Retrieval Via Technology-Assisted Review Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: High Recall Retrieval (HRR) tasks, including eDiscovery in the law, systematic literature reviews, and sunshine law requests focus on efficiently prioritizing relevant documents for human review.Technology-assisted review (TAR) refers to iterative human-in-the-loop workflows that combine human review with IR and AI techniques to minimize both time and manual effort while maximizing recall. |
Lenora Gray; David D. Lewis; Jeremy Pickens; Eugene Yang; |
| 359 | Large Language Model Powered Agents for Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various information retrieval fields, such as search engines, social networks, recommender systems, and conversational assistants. |
An Zhang; Yang Deng; Yankai Lin; Xu Chen; Ji-Rong Wen; Tat-Seng Chua; |
| 360 | Large Language Models for Recommendation: Past, Present, and Future Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We will explore how LLMs enhance recommender systems in terms of architecture, learning paradigms, and functionalities such as conversational abilities, generalization, planning, and content generation. The tutorial will shed light on the challenges and open problems in this burgeoning field, including trustworthiness, efficiency, online training, and evaluation of LLM4Rec. |
Keqin Bao; Jizhi Zhang; Xinyu Lin; Yang Zhang; Wenjie Wang; Fuli Feng; |
| 361 | Large Language Models for Tabular Data: Progresses and Future Directions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: By introducing methods of prompting or training cutting-edge LLMs for table interpreting, processing, reasoning, analytics, and generation, we aim to equip researchers and practitioners with the knowledge and tools needed to unlock the full potential of LLMs for tabular data in their domains. |
Haoyu Dong; Zhiruo Wang; |
| 362 | Preventing and Detecting Misinformation Generated By Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: As large language models (LLMs) become increasingly capable and widely deployed, the risk of them generating misinformation poses a critical challenge. Misinformation from LLMs … |
Aiwei Liu; Qiang Sheng; Xuming Hu; |
| 363 | Recent Advances in Generative Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Compared to the traditional index-retrieve-then-rank” pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). |
Yubao Tang; Ruqing Zhang; Zhaochun Ren; Jiafeng Guo; Maarten de Rijke; |
| 364 | Robust Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only … |
Yu-An Liu; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; |
| 365 | Search Under Uncertainty: Cognitive Biases and Heuristics: A Tutorial on Testing, Mitigating and Accounting for Cognitive Biases in Search Experiments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A growing body of empirical work exploring how people’s cognitive biases influence search and judgments, has led to the development of new models of search that draw upon Behavioural Economics and Psychology. This full day tutorial will provide a starting point for researchers seeking to learn more about information seeking, search and retrieval under uncertainty. |
Jiqun Liu; Leif Azzopardi; |
| 366 | Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The field of Quantum Computing (QC) has gained significant popularity in recent years, due to its potential to provide benefits in terms of efficiency and effectiveness when employed to solve certain computationally intensive tasks. In both Information Retrieval (IR) and Recommender Systems (RS) we are required to build methods that apply complex processing on large and heterogeneous datasets, it is natural therefore to wonder whether QC could also be applied to boost their performance. |
Maurizio Ferrari Dacrema; Andrea Pasin; Paolo Cremonesi; Nicola Ferro; |
| 367 | 5th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2024) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the 5th edition of this workshop we will provide a platform for researchers and industry to learn about novel and emerging technologies for semantic patent retrieval and big analytics employing sophisticated methods ranging from patent text mining, domain-specific information retrieval to large language models targeting next generation applications and use cases for the IP and related domains. |
Ralf Krestel; Hidir Aras; Linda Andersson; Florina Piroi; Allan Hanbury; Dean Alderucci; |
| 368 | AgentIR: 1st Workshop on Agent-based Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose the first Agent-based IR workshop at SIGIR 2024, as a continuation from one of the most successful IR workshops, DRL4IR. |
Qingpeng Cai; Xiangyu Zhao; Ling Pan; Xin Xin; Jin Huang; Weinan Zhang; Li Zhao; Dawei Yin; Grace Hui Yang; |
| 369 | Gen-IR @ SIGIR 2024: The Second Workshop on Generative Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Interest in this area across academia and industry is only expected to continue to grow as new research challenges and application opportunities arise. The goal of this proposed workshop, The Second Workshop on Generative Information Retrieval (Gen-IR @ SIGIR 2024) is to provide an interactive venue for exploring a broad range of foundational and applied Gen-IR research. |
Gabriel B\'{e}n\'{e}dict; Ruqing Zhang; Donald Metzler; Andrew Yates; Ziyan Jiang; |
| 370 | International Workshop on Algorithmic Bias in Search and Recommendation (BIAS) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Defining, assessing, and mitigating these biases throughout experimental pipelines is a primary step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. This workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. |
Alejandro Bellog\'{I}n; Ludovico Boratto; Styliani Kleanthous; Elisabeth Lex; Francesca Maridina Malloci; Mirko Marras; |
| 371 | IR-RAG @ SIGIR24: Information Retrieval’s Role in RAG Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: At the end of the workshop, we aim to have a clearer understanding of how robust information retrieval mechanisms can significantly enhance the capabilities of RAG systems. |
Fabio Petroni; Federico Siciliano; Fabrizio Silvestri; Giovanni Trappolini; |
| 372 | LLM4Eval: Large Language Model for Evaluation in IR Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main goal of LLM4Eval workshop is to bring together researchers from industry and academia to discuss various aspects of LLMs for evaluation in information retrieval, including automated judgments, retrieval-augmented generation pipeline evaluation, altering human evaluation, robustness, and trustworthiness of LLMs for evaluation in addition to their impact on real-world applications. |
Hossein A. Rahmani; Clemencia Siro; Mohammad Aliannejadi; Nick Craswell; Charles L. A. Clarke; Guglielmo Faggioli; Bhaskar Mitra; Paul Thomas; Emine Yilmaz; |
| 373 | MANILA24: SIGIR 2024 Workshop on Information Retrieval and Climate Impact Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The workshop aims to foster collaboration by bringing communities together that have so far not been very well connected — IR, systematic reviews, and climate change. |
Bart van den Hurk; Maarten de Rijke; Flora Salim; |
| 374 | Multimodal Representation and Retrieval [MRR 2024] Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this workshop, we target to bring a new retrieval problem where both queries and documents are multimodal. |
Xinliang Zhu; Arnab Dhua; Douglas Gray; I. Zeki Yalniz; Tan Yu; Mohamed Elhoseiny; Bryan Plummer; |
| 375 | ReNeuIR at SIGIR 2024: The Third Workshop on Reaching Efficiency in Neural Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, efficiency is no longer limited to time and space but has found new, challenging dimensions that stretch to resource, sample, and energy efficiency and have implications for users, researchers, and the environment. Examining algorithms and models through the lens of efficiency and its trade-off with effectiveness requires revisiting and establishing new standards and principles, from defining relevant concepts, to designing measures, to creating guidelines for making sense of the significance of findings. |
Maik Fr\{o}be; Joel Mackenzie; Bhaskar Mitra; Franco Maria Nardini; Martin Potthast; |
| 376 | SIGIR 2024 Workshop on ECommerce (ECOM24) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The workshop aims to foster collaboration, to attract research funding, and to introduce IR researchers and postgraduate students to eCommerce product discovery. |
Surya Kallumadi; Yubin Kim; Tracy Holloway King; Maarten de Rijke; Vamsi Salaka; |
| 377 | SIGIR 2024 Workshop on Simulations for Information Access (Sim4IA 2024) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building on recent developments in methods and toolkits, the Sim4IA workshop aims to bring together researchers and practitioners to form an interactive and engaging forum for discussions on the future perspectives of the field. |
Philipp Schaer; Christin Katharina Kreutz; Krisztian Balog; Timo Breuer; Norbert Fuhr; |
| 378 | The Second Workshop on Large Language Models for Individuals, Groups, and Society Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This is the second workshop in the series which discusses the cutting-edge developments in research and applications of personalizing large language models (LLMs) and adapting them to the demands of diverse user populations and societal needs. |
Michael Bendersky; Cheng Li; Qiaozhu Mei; Vanessa Murdock; Jie Tang; Hongning Wang; Hamed Zamani; Mingyang Zhang; Xingjian Zhang; |
| 379 | Third Workshop on Personalization and Recommendations in Search (PaRiS) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The purpose of this workshop is have a forum where latest research and advancements specifically on Personalization and Recommendations in Search (PaRiS) can be discussed in conjunction with SIGIR 2024. |
Sudarshan Lamkhede; Hamed Zamani; Moumita Bhattacharya; Hongning Wang; |
| 380 | A Predictive Framework for Query Reformulation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we investigate user query reformulation behavior. |
Reyhaneh Goli; |
| 381 | Axiomatic Guidance for Efficient and Controlled Neural Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We posit that by allowing external features to influence the semantic interactions within neural search at inference time, we can not only allow control over system output but reduce the need to model corpus-specific priors, which can instead be modelled by external features, allowing for greater generalisation and training efficiency gains. We aim to reduce the complexity of neural ranker training and inference, applying classical IR principles and systems that align with such principles as a generalisable process as opposed to the ad-hoc constraint of prior work. |
Andrew Parry; |
| 382 | GOLF: Goal-Oriented Long-term LiFe Tasks Supported By Human-AI Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The long-term tasks encompass broader personal life goals or development in aspects like health, finances, education, and professional development, which cannot be fully completed by LLM agents but require significant human involvement.This study introduces the GOLF framework (Goal-Oriented Long-term liFe tasks), which focuses on enhancing LLMs’ ability to assist in significant life decisions through goal orientation and long-term planning. |
Ben Wang; |
| 383 | Leveraging LLMs for Detecting and Modeling The Propagation of Misinformation in Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our second thread of work, we explore ways of creating synthetic datasets to eventually train supervised or few-shot example-based models. |
Payel Santra; |
| 384 | Machine Generated Explanations and Their Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given the preliminary system we aim to test the system across a range of tasks with correspondence to the augmented intelligence paradigm such as multi-hop question answering. |
Edward Richards; |
| 385 | Mosaicing Prevention in Declassification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This inference prevention task is motivated by what has been called the mosaicing” problem in declassification review for documents that in the past were withheld from public access for national security reasons~citepozen2005mosaic. The goal of such a review is to reveal as much as can now be safely revealed but to also withhold things that could be used to infer facts that require continued protection. |
Nathaniel Rollings; |
| 386 | Personalized Large Language Models Through Parameter Efficient Fine-Tuning Techniques Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, fine-tuning such systems is computationally expensive: since they are characterized by billions of parameters, the fine-tuning process has introduced profound computational challenges. For these reasons, we propose a novel approach that combines personalization and Parameter Efficient Fine-Tuning methods. |
Marco Braga; |
| 387 | Query Performance Prediction for Conversational Search and Beyond Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the limitation, I have proposed a QPP framework using automatically <u>gen</u>erated <u>re</u>evance judgments (QPP-GenRE); it decomposes QPP into independent subtasks of judging the relevance of each item in a ranked list to a given query [6]. |
Chuan Meng; |
| 388 | Towards A Framework for Legal Case Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This has been attributed to factors such as lack of structure, and lengthiness of case report documents, and queries formulated to represent an ongoing case for which the reports are being sought [4]. To address these factors we propose an IR framework that focuses on infusing structure into the documents and queries through the identification of legal rhetorical roles such as arguments and facts in the text. |
Tebo Leburu-Dingalo; |