PBiLoss is a model-agnostic regularization loss with PopPos and PopNeg sampling that reduces popularity bias metrics PRU and PRI by up to 10% in GNN recommenders while preserving accuracy on datasets like MovieLens.
How attentive are graph attention networks? In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29
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PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
PBiLoss is a model-agnostic regularization loss with PopPos and PopNeg sampling that reduces popularity bias metrics PRU and PRI by up to 10% in GNN recommenders while preserving accuracy on datasets like MovieLens.