Tian-Yu Zhu

Mitigating Popularity Bias in Recommendation with Global Listwise Learning and Progressive Bi-Weighting

In recommender systems, user feedback typically follows a long-tail distribution, which leads many recommendation algorithms to exacerbate popularity bias by disproportionately favoring popular items. To mitigate this issue, recent studies have employed Inverse Propensity Scoring (IPS) to rebalance training data via re...