DBA-SR: A Denoised and Bias-Aware Framework for Social Recommendation

Social recommendation leverages social relations to complement user–item interactions, alleviating data sparsity in recommendation systems. However, existing approaches still suffer from two critical issues: social graphs inevitably contain noisy or redundant edges, which can hinder user representation learning, and the results of recommendations are biased toward popular items, failing to capture user preferences for long-tail items. To address these challenges, we propose a novel model named denoised and bias-aware framework for social recommendation (DBA-SR). DBA-SR incorporates two key innovations. First, a structure-aware social graph pruning module adaptively removes weak or redundant edges to construct a denoised and structurally informative social graph. Second, a tail- and popularity-aware preference modeling mechanism jointly enhances long-tail preference learning and suppresses popularity bias through dedicated regularization strategies. The overall model is trained under a unified dual graph reconstruction and variational regularization objective. Extensive experiments on three real-world benchmark datasets demonstrate that DBA-SR consistently outperforms baselines in top-Nrecommendation tasks. Further ablation studies confirm the complementary effects of structure-aware pruning and preference specialization. This work provides new insights into the role of structural denoising in social graphs and demonstrates an effective solution to mitigating popularity bias.