214 citations · 1k across the 16 of their papers we have counts for
7 papers · 1 filter
Graph Collaborative Reasoning
Hanxiong Chen, Yunqi Li, Shaoyun Shi +3
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. Ho…
Counterfactual Explainable Recommendation
Juntao Tan, Shuyuan Xu, Yingqiang Ge +3
By providing explanations for users and system designers to facilitate better understanding and decision making, explainable recommendation has been an important research problem.…
Personalized Counterfactual Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Shuyuan Xu +2
Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore…
User-oriented Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu +2
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects…
Causal Collaborative Filtering
Shuyuan Xu, Yingqiang Ge, Yunqi Li +3
Many of the traditional recommendation algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data to estimate the user-item correlat…
Discrete Knowledge Graph Embedding based on Discrete Optimization
Yunqi Li, Shuyuan Xu, Bo Liu +4
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable di…
Towards Long-term Fairness in Recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao +8
As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior appro…