User behaviors and social interactions on online platforms are intricately intertwined, naturally forming complex graph structures. Leveraging this structure, Graph Neural Networks (GNNs) efficiently aggregate neighborhood information and have become a prevailing paradigm for social recommendation. However, existing methods often overemphasize social modeling while overlooking the joint effects of preference-guided relation filtering and user/item biases, rendering them vulnerable to noise from redundant ties. To address these limitations, we propose PDDSR, a Preference-Guided Debiasing and Denoising Social Recommendation framework. Specifically, for debiasing, PDDSR explicitly models user rating bias and item popularity bias as learnable vectors, integrating them into embedding learning to mitigate bias drift at the embedding level. Simultaneously, for denoising, the model employs a social relation confidence mechanism guided by user preferences and adopts an adaptive graph denoising strategy to retain highly informative connections, effectively capturing social influence while filtering out noise. Extensive experiments on the Ciao and Epinions datasets demonstrate that PDDSR consistently outperforms state-of-the-art methods, and notably on the Ciao dataset, the MAE and RMSE are improved by 1.90% and 1.87%, respectively. These results validate the effectiveness and robustness of the joint debiasing and denoising mechanism in complex social recommendation scenarios.
Li et al. (Tue,) studied this question.