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.…