Disentangled Graph Contrastive Learning for Socially-Aware Next-Item Recommendation

Next-item recommendation has received considerable attention in academia and industry, which aims to predict the next desired item for each user based on his historical behaviors. It has been validated that user behaviors are driven by two key factors: social influence, which leverages social relationships to better infer user preference, and sequential influence, which captures item transition patterns to model the dynamic evolution of user interest. While previous next-item recommenders have made great progress, we argue that there still exist three critical limitations: (1) they always follow the paradigm of entangling social and sequential influences, resulting in poor interpretability; (2) they fail to explicitly capture high-order influences at social-level and sequential-level, resulting in the suboptimal performance; (3) they fail to dynamically distinguish the importance of each influence factor when predicting user preference. To settle these three defects, we contribute a novel solution for socially-aware next-item recommendation, namely Disentangled Graph Contrastive Learning (DGCL) method, which explicitly disentangles social and sequential influences on user behavior data. Specifically, we first reorganize social relationships and all users' behavior sequences as two separate graphs. Then, a disentangled graph propagation module is developed on these two graphs to independently capture high-order influences at social-level and sequential-level. Furthermore, we formalize a dual contrastive learning paradigm as an auxiliary task to supervise the thorough disentanglement. Finally, we devise a user-specific attention mechanism to adaptively differentiate the importance of each influence factor for model prediction. Empirical results on four benchmark datasets demonstrate the superiority of DGCL over recent state-of-the-art recommenders. Further analysis verifies the rationality and necessity of each part in our solution. Our implemented codes and used datasets are available at https://github.com/wubinzzu/DGCL.