Social Relation Enhanced Heterogeneous Graph Contrastive Learning for Recommendation

Recommender systems have emerged as pivotal components in numerous online services, facilitating the personalized discovery of items that align with users' interests. These systems have showcased their significance in diverse scenarios, with particular prominence observed in applications related to social networks. Heterogeneous Graph Neural Networks (HGNNs) have shown success in recommendation tasks by embedding rich semantics from different relations into latent representations. However, the representation power of existing HGNNs is often limited by sparse data availability, particularly for sparse interaction labels during optimization. To address these challenges, we propose a novel self-supervised learning model called Social Relation-based Heterogeneous Graph Contrastive Learning (SR-HGCL). Our approach merges the user-user social graph and the user-item interaction graph into a unified heterogeneous graph, creating the heterogeneous view. We also construct the social relation enhanced view by resampling the user-item interaction graph. In the learning process, we leverage metapath based graph learning and graph diffusion with attention to obtain multi-view embeddings for users and items. Additionally, we incorporate view-level contrastive learning to encourage distinct embeddings from different views, improving interpretability. We evaluate SR-HGCL on three benchmark datasets and demonstrate its superiority over state-of-the-art methods.