75 citations · 95 across the 6 of their papers we have counts for
5 papers
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Juntao Tan, Yingqiang Ge, Yan Zhu +4
Modern recommender systems utilize users' historical behaviors to generate personalized recommendations. However, these systems often lack user controllability, leading to diminish…
Automated Data Denoising for Recommendation
Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane +3
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback. Due to the issue of data…
Fairness-aware Differentially Private Collaborative Filtering
Zhenhuan Yang, Yingqiang Ge, Congzhe Su +3
Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes w…
Causal Inference for Recommendation: Foundations, Methods and Applications
Shuyuan Xu, Jianchao Ji, Yunqi Li +3
Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recomme…
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…