Modeling Multi-View Interactions with Contrastive Graph Learning for Collaborative Filtering

Graph Neural Networks (GNNs) and its many variants have recently been successfully utilized to tackle various recommendation tasks. Although effective, existing methods still face several limitations. First, supervision signal sparsity makes it difficult for them to learn high-quality representations and optimize the model parameters. Second, the learned representations are vulnerable to noisy interactions, as the neighborhood aggregation scheme increases the impact of observed edges. To alleviate these issues, we exploit self-supervised learning (SSL) to learn more accurate and robust representations from the user-item interaction graph. Particularly, we propose a novel SSL model that effectively integrates contrastive multi-view learning and pseudo-siamese network to construct a pre-training and post-training framework. Moreover, we present three data augmentation techniques during the pre-training stage and explore the effects of combining different augmentations, which allow us to learn more general and robust representations for the recommendation. Experimental evaluations on two real-world datasets show that the proposed solution significantly improves the recommendation accuracy, especially for sparse supervision signal, and is also noise resistant.