Diffusion-Augmented Graph Contrastive Learning for Knowledge-Aware Recommendation

Knowledge graph (KG) contrastive learning (CL) has garnered significant attention in the realm of recommendation systems. However, existing models often employ random masking for graph enhancement, which can introduce sampling bias and impede interpretability. Furthermore, the KG-UIG information imbalance can lead to the neglect of critical information in the user-item interaction graph (UIG) by the model. To address these challenges, we propose a novel model, diffusion-augmented graph CL (DAGCL). This model leverages a graph diffusion mechanism for data enhancement in CL, thereby ensuring that the generated diffusion graph closely resembles the original UIG and avoiding the pitfalls associated with random sampling. Additionally, DAGCL enhances the impact of UIG on predictive accuracy by implementing both intragraph and intergraph CL (GCL), effectively mitigating the information imbalance between KGs and UIG. The model also leverages the structural characteristics of the UIG to construct a structural diffusion graph, which is integrated with the information diffusion graph to produce a comprehensive diffusion representation-further enhancing the model's robustness against sampling noise and semantic dilution by preserving essential interaction patterns and structural features in the augmented graph. Experimental results across three real-world datasets demonstrate that our proposed model outperforms state-of-the-art models significantly.