214 citations · 401 across the 8 of their papers we have counts for
8 papers
Transferable Fairness for Cold-Start Recommendation
Yunqi Li, Dingxian Wang, Hanxiong Chen +1
With the increasing use and impact of recommender systems in our daily lives, how to achieve fairness in recommendation has become an important problem. Previous works on fairness-…
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
Hanxiong Chen, Yunqi Li, He Zhu +1
Human intelligence is able to first learn some basic skills for solving basic problems and then assemble such basic skills into complex skills for solving complex or new problems.…
Fairness in Recommendation: Foundations, Methods and Applications
Yunqi Li, Hanxiong Chen, Shuyuan Xu +4
As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and t…
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…
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…
Generate Natural Language Explanations for Recommendation
Hanxiong Chen, Xu Chen, Shaoyun Shi +1
Providing personalized explanations for recommendations can help users to understand the underlying insight of the recommendation results, which is helpful to the effectiveness, tr…