Self-supervised Hybrid Graph Neural Network for Session-Based Recommendation

Abstract

Session-based recommendation aims to predict user actions based on anonymous sessions. Most of the existing session recommendation algorithms based on graph neural network (GNN) only extract user preferences for the current session, but ignore the high-order multivariate relationships from other sessions, which affects the recommendation accuracy. Moreover, session-based recommendation suffers more from the problem of data sparsity due to the very limited short-term interactions. To solve the above problems, this paper proposes a model named self-  supervised hybrid graph neural network (SHGN) for session-based recommendation. Firstly, the model describes the relationship between sessions and objects by constructing the original data into three views. Next, a graph attention network is used to capture the low-order transitions information of items within a session, and then a residual graph convolutional network is proposed to mine the high-order transitions information of items and sessions. Finally, self-supervised learning (SSL) is integrated as an auxiliary task. By maximizing the mutual information of session embeddings learnt from different views, data augmentation is performed to improve the recommendation performance. In order to verify the effectiveness of the proposed method, comparative experiments with mainstream baseline models such as SR-GNN, GCE-GNN and DHCN are carried out on four benchmark datasets of Tmall, Diginetica, Nowplaying and Yoochoose, and the results are improved in P@20, MRR@20 and other performance indices.