MVGCL: Multi-View Graph Contrastive Learning for Service Recommendation

In service recommender system, graph neural networks (GNNs) perform message passing through diffusion mechanism based on user-service relationship graph. However, existing GNN-based service recommendation models suffer from two limitations: ❨1❩ message passing is only carried out at firstorder neighbors, as higher-order may cause over-smoothing phenomenon, confining feature propagation in GNNs; and ❨2❩ due to sparse and noisy interactions, the distribution of embedding vectors is nonuniform in the latent space, resulting in unsatisfactory performance for downstream applications. To this end, we propose a fixed global graph diffusion view that is independent of the original user-service observed local view to form a multi-view learning by building contrastive learning (CL) relationship, named as Multi-View Graph Contrastive Learning (MVGCL). Specifically, it enhances the capability of message passing through constructed local and global multi-view graphs, and alleviates the sparse and noisy influences by performing intra-CL within local/global view and inter-CL between multi-view to obtain a more uniform distribution of user and service node representations. Extensive experiments are conducted on three benchmark datasets within different scales, and the results demonstrate that our proposed MVGCL can remarkably outperforms state-of-the-art competing baselines on various evaluation metrics.