activity
20192023
most citedReinforcement Knowledge Graph Reasoning for Explainable Recommendation

440 citations · 1.1k across the 26 of their papers we have counts for

collaborators
Showing 2022Show all

5 papers · 1 filter

cs.IR2022★ 19 cited

Dynamic Causal Collaborative Filtering

Shuyuan Xu, Juntao Tan, Zuohui Fu +3

Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loo…

cs.IR2022★ 28 cited

A Survey on Trustworthy Recommender Systems

Yingqiang Ge, Shuchang Liu, Zuohui Fu +6

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. How…

cs.IR2022★ 55 cited

Explainable Fairness in Recommendation

Yingqiang Ge, Juntao Tan, Yan Zhu +7

Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies…

cs.IR2022★ 5 cited

Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

Shijie Geng, Shuchang Liu, Zuohui Fu +2

For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned kno…

cs.IR2022★ 101 cited

Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

Juntao Tan, Shijie Geng, Zuohui Fu +4

Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the comp…