5 citations · 11 across the 6 of their papers we have counts for
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cs.AI2022
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…
cs.IR2022★ 3 cited
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…
cs.AI2022★ 3 cited
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…