arXiv paper 2605.11145 proposes DPAA, a debiasing framework for GNN-based CF that applies adaptive weighting during message passing, outperforming prior methods.

arXiv paper 2605.11145, submitted 11 May 2026, proposes DPAA to debias GNN-based collaborative filtering. The framework applies adaptive embedding-aware weights during message passing to counter popularity amplification.

Key facts

arXiv paper 2605.11145 submitted 11 May 2026.

DPAA applies adaptive embedding-aware weights during message passing.

Prior debiasing methods fail to address aggregation-level bias.

Layer-wise weighting amplifies higher-order neighborhoods.

Outperforms state-of-the-art GNN debiasing on real-world datasets.

Graph neural networks (GNNs) have become the backbone of collaborative filtering (CF) in recommender systems, propagating user-item signals over interaction graphs with strong results.…