214 citations · 1.5k across the 43 of their papers we have counts for
7 papers · 1 filter
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…
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…
AutoLossGen: Automatic Loss Function Generation for Recommender Systems
Zelong Li, Jianchao Ji, Yingqiang Ge +1
In recommendation systems, the choice of loss function is critical since a good loss may significantly improve the model performance. However, manually designing a good loss is a b…
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…
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…
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…
Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning
Yingqiang Ge, Xiaoting Zhao, Lucia Yu +4
The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware r…