Diffusion-Based Graph Contrastive Learning for Recommendation with Implicit Feedback

Recent studies on self-supervised learning with graph-based recommendation models have achieved outstanding performance. They usually introduce auxiliary learning tasks that maximize the mutual information between representations of the original graph and its augmented views. However, most of these models adopt random dropout to construct the additional graph view, failing to differentiate the importance of edges. The insufficiency of these methods in capturing structural properties of the user-item interaction graph leads to suboptimal recommendation performance. In this paper, we propose a Graph Diffusion Contrastive Learning (GDCL) framework for recommendation to close this gap. Specifically, we perform graph diffusion on the user-item interaction graph. Then, the diffusion graph is encoded to preserve its heterogeneity by learning a dedicated representation for every type of relations. A symmetric contrastive learning objective is used to contrast local node representations of the diffusion graph with those of the user-item interaction graph for learning better user and item representations. Extensive experiments on real datasets demonstrate that GDCL consistently outperforms state-of-the-art recommendation methods.