DiffGCL: Diffusion model-based Graph Contrastive Learning for Service Recommendation

Recent years have witnessed the notable resurgence of graph neural networks (GNNs) in service recommender systems. GNN-based service recommender systems heavily rely on the recursive message propagation mechanism between layers. However, they encounter challenges due to data sparsity and susceptibility to noisy interactions, leading to unstable and suboptimal performance. Several studies have attempted to mitigate these issues by integrating GNNs with contrastive learning techniques. Despite their success, most existing approaches utilize either stochastic or heuristic data augmentation methods. We argue that these methods suffer from inherent limitations, including disruption of the original structure of the user-service interaction graph and the need for laborious and iterative experimentation. In this paper, we propose a novel Diffusion model-based Graph Contrastive Learning framework, named DiffGCL, for service recommendation. Specifically, we incorporate Diffusion models into service recommender systems as generators for contrastive learning views. We also introduce a self-adaptive data augmentation strategy that dynamically constructs new dual views each epoch while preserving the intrinsic semantic structure of the generated graph. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of DiffGCL, outperforming various baseline models on service recommendation tasks.