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20222024
most citedExplainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

46 citations · 153 across the 16 of their papers we have counts for

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13 papers · 1 filter

cs.IR20242 cited

Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method

Chen Yang, Sunhao Dai, Yupeng Hou +4

Reciprocal recommender systems~(RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However…

cs.IR202417 cited

Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning

Jiakai Tang, Sunhao Dai, Zexu Sun +6

In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. Howeve…

cs.IR2024

Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration

Sunhao Dai, Weihao Liu, Yuqi Zhou +6

The proliferation of Large Language Models (LLMs) has led to an influx of AI-generated content (AIGC) on the internet, transforming the corpus of Information Retrieval (IR) systems…

cs.IR2024

QAGCF: Graph Collaborative Filtering for Q&A Recommendation

Changshuo Zhang, Teng Shi, Xiao Zhang +5

Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one i…

cs.IR2024

FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval

Chen Xu, Jun Xu, Yiming Ding +2

In pursuit of fairness and balanced development, recommender systems (RS) often prioritize group fairness, ensuring that specific groups maintain a minimum level of exposure over a…

cs.IR2024

List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation

Shicheng Xu, Liang Pang, Jun Xu +2

The results of information retrieval (IR) are usually presented in the form of a ranked list of candidate documents, such as web search for humans and retrieval-augmented generatio…