Self-Attention Based Sequential Recommendation With Graph Convolutional Networks

Learning embeddings representations of users and items lies at the core of modern recommender systems. Existing methods based on Graph Convolutional Network (GCN) and sequential recommendation typically obtain a user’s or an item’s embedding by mapping from pre-existing features into better embeddings for users and items, such as ID and attributes. GCN integrates the user-item interaction as the bipartite graph structure into the embedding process, which can better represent sparse data, but cannot capture users’ long-term interests. Sequential recommendation seek to capture the “context” of users’ activities based on their historical actions, but requires dense data to support it. The goal of our work is to combine the advantages of GCN and sequential recommendation models by proposing a novel Self-Attention based Sequential recommendation with Graph Convolutional Networks (SASGCN). It uses multiple lightweight GCN layers to capture high-order connectivity between users and items, and by introducing ratings as auxiliary information into the user-item interaction matrix, it provides richer information. By incorporating self-attention based methods, the proposed model capture long-term semantics through relatively few actions. Extensive experiments on three benchmark datasets show that our model outperforms various state-of-the-art models consistently.