46 citations · 68 across the 6 of their papers we have counts for
5 papers · 1 filter
Enhancing Recommendation Explanations through User-Centric Refinement
Jingsen Zhang, Zihang Tian, Xueyang Feng +1
Generating natural language explanations for recommendations has become increasingly important in recommender systems. Traditional approaches typically treat user reviews as ground…
On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
Jiayan Guo, Lun Du, Xu Chen +5
Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF h…
Dually Enhanced Propensity Score Estimation in Sequential Recommendation
Chen Xu, Jun Xu, Xu Chen +2
Sequential recommender systems train their models based on a large amount of implicit user feedback data and may be subject to biases when users are systematically under/over-expos…
Fairness-aware Cross-Domain Recommendation
Jiakai Tang, Xu Chen, Xueyang Feng
Cross-Domain Recommendation (CDR) is an effective way to alleviate the cold-start problem. However, previous work severely ignores fairness and bias when learning the mapping funct…
Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction
Weijie Yu, Zhongxiang Sun, Jun Xu +4
As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching r…