AI RESEARCH
PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
arXiv CS.AI
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ArXi:2507.19067v2 Announce Type: replace-cross Recommender systems based on graph neural networks (GNNs) have been proved to perform well on user-item interactions. However, they commonly suffer from popularity bias -- the tendency to over-recommend popular items -- resulting in less personalization, unfair exposure and lower recommendation diversity.