A self-supervised learning framework for sequential recommendation

Sequential recommendation that aims to predict user preference with historical user interactions becomes one of the most popular tasks in the recommendation area. The existing methods concentrated on user's sequential features among exposed items have achieved good performance. However, they only rely on single item prediction optimization to learn data representation, which ignores the association between context data and sequence data. In this paper, we propose a novel self-supervised learning based sequential recommendation network (SSLRN), which contrastively learns data correlation to promote data representation of users and items. We design two auxiliary contrastive learning tasks to regularize user and item representation based on mutual information maximization (MIM). In particular, the item contrastive learning captures sequential contrast feature with sequence-item MIM, and the user contrastive learning regularizes user latent representation with user-item MIM. We evaluate our model on five real-world datasets and the experimental results show that the proposed framework significantly and consistently outperforms state-of-the-art sequential recommendation techniques.