SDADL: SVD-Based Dual Adaptive Debiasing Learning for Personalized Recommendation

Abstract

Personalized recommendation systems often suffer from various biases in data-driven paradigms, which can significantly degrade their fairness and generalization. Existing debiasing approaches typically rely on strong assumptions about bias types or purely statistical corrections, limiting their adaptability to unknown and changeable bias patterns, which fail to capture the complexities of human behavior and decision-making. To address this challenge, we propose a novel SVD-based dual adaptive debiasing learning (SDADL) framework. In SDADL, we distinguish between target samples (TS), which are observed positive interactions in training, and flaw samples (FS), which are highly ranked but potentially spurious positives. During training, node embeddings are decomposed via singular value decomposition (SVD) and further refined through dual adaptive learning pathways dedicated to TS and FS, respectively. Notably, the model is not explicitly constrained by the loss on the original embeddings; instead, it adaptively balances dual path learning signals under weak supervision, thereby discovering and mitigating hidden biases in a self-guided manner. Experiments demonstrate that SDADL consistently enhances base GCN models in a plug-and-play form and is effective in both supervised and self-supervised learning scenarios.