Session-based Recommendation with Temporal Graph Neural Network and Contrastive Learning

Abstract

Session-based recommendation aims to predict the next clicked item using the current ongoing session of an anonymous user. Since the user's information is unknown, the available information is limited. In recent years, due to the excellent performance of graph neural networks in many applications, many works have applied graph neural networks to session-based recommendation. However, we found that the conversion of session data into graph-structured data is a lossy graph encoding method, which leads to the loss of item order information in the session. In addition, only one session can be used for recommendation in session-based recommendation. Compared with other recommendations, the problem of data sparsity is more serious. Self-supervised learning can discover ground-truth samples and has great potential in solving the data sparsity problem. Therefore, we propose a session-based recommendation model based on graph neural network and contrastive learning to solve the problem of information loss and data sparsity in graph encoding. Extensive experiments on three benchmark datasets demonstrate that our model outperforms other methods.