Graph Diffusive Self-Supervised Learning for Social Recommendation
Jiuqiang Li, Hongjun Wang
Abstract
Social recommendation aims at augmenting user-item interaction relationships and boosting recommendation quality by leveraging social information. Recently, self-supervised learning (SSL) has gained widespread adoption for social recommender. However, most existing methods exhibit poor robustness when faced with sparse user behavior data and are susceptible to inevitable social noise. To overcome the aforementioned limitations, we introduce a new Graph Diffusive Self-Supervised Learning (GDSSL) paradigm for social recommendation. Our approach involves the introduction of a guided social graph diffusion model that can adaptively mitigate the impact of social relation noise commonly found in real-world scenarios. This model progressively introduces random noise to the initial social graph and then iteratively restores it to recover the original structure. Additionally, to enhance robustness against noise and sparsity, we propose graph diffusive self-supervised learning, which utilizes the denoised social relation graph generated by our diffusion model for contrastive learning. The extensive experimental outcomes consistently indicate that our proposed GDSSL outmatches existing advanced solutions in social recommendation.