GC–HGNN: A global-context supported hypergraph neural network for enhancing session-based recommendation

Abstract

• In order to fully consider the global- and local-level context information, all session sequences are modeled as a hypergraph and the current session sequence is modeled as local session graph. In particular, each node in the local session graph constructed in this paper is connected to an implicit dynamic mediation node. • Our proposed model uses hypergraph convolutional neural network to capture complex higher-order relationships between items and graph attention network to learn the pairwise item transiting relationships. • In our work, sum-pooling operation is adopted to fuse the global and local-level context information, and reversed position information is incorporated. Specifically, the proposed model uses the attention mechanism to get the final session representation. • The results of extensive experiments demonstrate that our proposed model consistently outperforms the state-of-the-art methods. Session-based recommendation (SBR) aims to predict the next most likely item to be interacted based on the current session. Given a short individual session, it is difficult to make accurate recommendations by using only the information of the sequence itself. To improve the recommendation effect, it has become a trend to comprehensively consider the interactive information of other session sequences. In this paper, GC–HGNN, a hypergraph neural network, is proposed to enhance the SBR effects. The model first learns the preferences of current users by transiting information among items in all session sequences. On this basis, the GC–HGNN model fully considers the global context information and local context information of items, and constructs the global session hypergraph and local session graph. The global context information is obtained by the hypergraph convolutional neural network, and the graph attention network calculates the local context information by learning the pairwise relationship between items. The sum-pooling method is utilized to fuse the two kinds of information and the fused information is applied to accomplish the prediction. Extensive experiments were conducted on three public datasets, Diginetica, Tmall and Nowplaying, and the experimental results demonstrate that our proposed GC-HGNN model outperforms the baseline models in SBR.