Contrastive Learning Based Graph Convolution Network for Social Recommendation

Exploiting social networks is expected to enhance the performance of recommender systems when interaction information is sparse. Existing social recommendation models focus on modeling multi-graph structures and then aggregating the information from these multiple graphs to learn potential user preferences. However, these methods often employ complex models and redundant parameters to get a slight performance improvement. Contrastive learning has been widely researched as an effective paradigm in the area of recommendation. Most existing contrastive learning-based models usually focus on constructing multi-graph structures to perform graph augmentation for contrastive learning. However, the effect of graph augmentation on contrastive learning is inconclusive. In view of these challenges, in this work, we propose a contrastive learning based graph convolution network for social recommendation (CLSR), which integrates information from both the social graph and the interaction graph. First, we propose a fusion-simplified method to combine the social graph and the interaction graph. Technically, on the basis of exploring users’ interests by interaction graph, we further exploit social connections to alleviate data sparsity. By combining the user embeddings learned through two graphs in a certain proportion, we can obtain user representation at a finer granularity. Meanwhile, we introduce a contrastive learning framework for multi-graph network modeling, where we explore the feasibility of constructing positive and negative samples of contrastive learning by conducting data augmentation on embedding representations. Extensive experiments verify the superiority of CLSR’s contrastive learning framework and fusion-simplified method of integrating social relations.