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- short-paperJuly 2024
AgentIR: 1st Workshop on Agent-based Information Retrieval
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2024, Pages 3025–3028https://doi.org/10.1145/3626772.3657989Information retrieval (IR) systems have become an essential component in modern society to help users find useful information, which consists of a series of processes including query expansion, item recall, item ranking and re-ranking, etc. Based on the ...
- research-articleJuly 2024
GraphGPT: Graph Instruction Tuning for Large Language Models
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2024, Pages 491–500https://doi.org/10.1145/3626772.3657775Graph Neural Networks (GNNs) have evolved to understand graph structures through recursive exchanges and aggregations among nodes. To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation. Traditional methods ...
- research-articleJuly 2024
Unbiased Learning-to-Rank Needs Unconfounded Propensity Estimation
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2024, Pages 1535–1545https://doi.org/10.1145/3626772.3657772The logs of the use of a search engine provide sufficient data to train a better ranker. However, it is well known that such implicit feedback reflects biases, and in particular a presentation bias that favors higher-ranked results. Unbiased Learning-to-...
- research-articleJuly 2024
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback
- Qian Dong,
- Yiding Liu,
- Qingyao Ai,
- Zhijing Wu,
- Haitao Li,
- Yiqun Liu,
- Shuaiqiang Wang,
- Dawei Yin,
- Shaoping Ma
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2024, Pages 48–58https://doi.org/10.1145/3626772.3657689Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR). However, the responses generated by off-the-shelf LLMs tend to be generic, i.e., cannot capture the ...
- research-articleJune 2024
Toward Bias-Agnostic Recommender Systems: A Universal Generative Framework
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 6Article No.: 142, Pages 1–30https://doi.org/10.1145/3655617User behavior data, such as ratings and clicks, has been widely used to build personalizing models for recommender systems. However, many unflattering factors (e.g., popularity, ranking position, users’ selection) significantly affect the performance of ...
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- tutorialMay 2024
Large Language Models for Graphs: Progresses and Directions
WWW '24: Companion Proceedings of the ACM on Web Conference 2024May 2024, Pages 1284–1287https://doi.org/10.1145/3589335.3641251Graph neural networks (GNNs) have emerged as fundamental methods for handling structured graph data in various domains, including citation networks, molecule prediction, and recommender systems. They enable the learning of informative node or graph ...
- research-articleMay 2024
Whole Page Unbiased Learning to Rank
WWW '24: Proceedings of the ACM on Web Conference 2024May 2024, Pages 1431–1440https://doi.org/10.1145/3589334.3645474The page presentation biases in the information retrieval system, especially on the click behavior, is a well-known challenge that hinders improving ranking models' performance with implicit user feedback. Unbiased Learning to Rank~(ULTR) algorithms are ...
- research-articleMay 2024
Representation Learning with Large Language Models for Recommendation
WWW '24: Proceedings of the ACM on Web Conference 2024May 2024, Pages 3464–3475https://doi.org/10.1145/3589334.3645458Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. However, these graph-based recommenders heavily depend on ID-based data, ...
- articleMarch 2024
Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs
- Zhikai Chen,
- Haitao Mao,
- Hang Li,
- Wei Jin,
- Hongzhi Wen,
- Xiaochi Wei,
- Shuaiqiang Wang,
- Dawei Yin,
- Wenqi Fan,
- Hui Liu,
- Jiliang Tang
ACM SIGKDD Explorations Newsletter (SIGKDD), Volume 25, Issue 2December 2023, Pages 42–61https://doi.org/10.1145/3655103.3655110Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text ...
- research-articleMarch 2024
LLMRec: Large Language Models with Graph Augmentation for Recommendation
WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data MiningMarch 2024, Pages 806–815https://doi.org/10.1145/3616855.3635853The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as noise, ...
- research-articleMarch 2024
Text-Video Retrieval via Multi-Modal Hypergraph Networks
WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data MiningMarch 2024, Pages 369–377https://doi.org/10.1145/3616855.3635757Text-video retrieval is a challenging task that aims to identify relevant videos given textual queries. Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and ...
- surveyFebruary 2024
Explainability for Large Language Models: A Survey
- Haiyan Zhao,
- Hanjie Chen,
- Fan Yang,
- Ninghao Liu,
- Huiqi Deng,
- Hengyi Cai,
- Shuaiqiang Wang,
- Dawei Yin,
- Mengnan Du
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 15, Issue 2Article No.: 20, Pages 1–38https://doi.org/10.1145/3639372Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, ...
- research-articleJanuary 2024
SMEC: Scene Mining for E-Commerce
Journal of Computer Science and Technology (JCST), Volume 39, Issue 1Feb 2024, Pages 192–210https://doi.org/10.1007/s11390-021-1277-0AbstractScene-based recommendation has proven its usefulness in E-commerce, by recommending commodities based on a given scene. However, scenes are typically unknown in advance, which necessitates scene discovery for E-commerce. In this article, we study ...
- research-articleJanuary 2024
SMLP4Rec: An Efficient All-MLP Architecture for Sequential Recommendations
ACM Transactions on Information Systems (TOIS), Volume 42, Issue 3Article No.: 86, Pages 1–23https://doi.org/10.1145/3637871Self-attention models have achieved the state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user–item interactions. However, they rely on adding positional embeddings to the item sequence to retain ...
- research-articleMay 2024
Learning to tokenize for generative retrieval
- Weiwei Sun,
- Lingyong Yan,
- Zheng Chen,
- Shuaiqiang Wang,
- Haichao Zhu,
- Pengjie Ren,
- Zhumin Chen,
- Dawei Yin,
- Maarten de Rijke,
- Zhaochun Ren
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing SystemsDecember 2023, Article No.: 2010, Pages 46345–46361As a new paradigm in information retrieval, generative retrieval directly generates a ranked list of document identifiers (docids) for a given query using generative language models (LMs). How to assign each document a unique docid (denoted as document ...
- research-articleMay 2024
Evaluating graph neural networks for link prediction: current pitfalls and new benchmarking
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing SystemsDecember 2023, Article No.: 169, Pages 3853–3866Link prediction attempts to predict whether an unseen edge exists based on only a portion of edges of a graph. A flurry of methods have been introduced in recent years that attempt to make use of graph neural networks (GNNs) for this task. Furthermore, ...
- research-articleNovember 2023Honorable Mention
Sequential Recommendation with User Evolving Preference Decomposition
SIGIR-AP '23: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific RegionNovember 2023, Pages 253–263https://doi.org/10.1145/3624918.3625312Modeling user sequential behaviors has recently attracted increasing attention in the recommendation domain. Existing methods mostly assume coherent preference in the same sequence. However, user personalities are volatile and easily changed, and there ...
- short-paperOctober 2023
DRL4IR: 4th Workshop on Deep Reinforcement Learning for Information Retrieval
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementOctober 2023, Pages 5304–5307https://doi.org/10.1145/3583780.3615303\AcIR is one of the most important fields to help users find relevant information. The interaction between IR systems and users can be naturally formulated as a decision-making problem. In the last decade, deep reinforcement learning (DRL) has become a ...
- research-articleOctober 2023
I3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementOctober 2023, Pages 441–451https://doi.org/10.1145/3583780.3614923Passage retrieval is a fundamental task in many information systems, such as web search and question answering, where both efficiency and effectiveness are critical concerns. In recent years, neural retrievers based on pre-trained language models (PLM), ...
- ArticleSeptember 2023
LtrGCN: Large-Scale Graph Convolutional Networks-Based Learning to Rank for Web Search
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo TrackSep 2023, Pages 635–651https://doi.org/10.1007/978-3-031-43427-3_38AbstractWhile traditional Learning to Rank (LTR) models use query-webpage pairs to perform regression tasks to predict the ranking scores, they usually fail to capture the structure of interactions between queries and webpages over an extremely large ...