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.
Mitigating popularity bias in recommendation with unbalanced interactions: A gradient perspective
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IR 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.