5 citations · 11 across the 6 of their papers we have counts for
6 papers
Uncertainty-Aware Instance Reweighting for Off-Policy Learning
Xiaoying Zhang, Junpu Chen, Hongning Wang +4
Off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has shown importance in various real-world applications, such as se…
Disentangled Representation for Diversified Recommendations
Xiaoying Zhang, Hongning Wang, Hang Li
Accuracy and diversity have long been considered to be two conflicting goals for recommendations. We point out, however, that as the diversity is typically measured by certain pre-…
Learning to Counterfactually Explain Recommendations
Yuanshun Yao, Chong Wang, Hang Li
Recommender system practitioners are facing increasing pressure to explain recommendations. We explore how to explain recommendations using counterfactual logic, i.e. "Had you not…
Forgetting Fast in Recommender Systems
Wenyan Liu, Juncheng Wan, Xiaoling Wang +3
Users of a recommender system may want part of their data being deleted, not only from the data repository but also from the underlying machine learning model, for privacy or utili…
Counterfactually Evaluating Explanations in Recommender Systems
Yuanshun Yao, Chong Wang, Hang Li
Modern recommender systems face an increasing need to explain their recommendations. Despite considerable progress in this area, evaluating the quality of explanations remains a si…
Conversational Contextual Bandit: Algorithm and Application
Xiaoying Zhang, Hong Xie, Hang Li +1
Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. How…