Hypergraph Neural Networks with Attention Mechanism for Session-based Recommendation

Abstract

Abstract The session-based recommendation task is designed to predict the behavior of the current session at the next moment based on multiple anonymous sessions. Due to the lack of user information in the session, the traditional recommendation model cannot be used directly to model the interest of specific users. In this paper, a session recommendation model based on hypergraph neural networks and attention mechanism (HGNNA) is proposed. Firstly, the features of items are learned by constructing hypergraph neural networks, then the conversation information is aggregated by self-attention mechanism, and finally the information among similar sessions is aggregated by graph attention networks. The hypergraph neural networks can capture the correlation between items, the self-attention mechanism can show the interest of the current session, and the graph attention networks can find the interest pattern between similar sessions, so that the representation vector of the session includes the information of the items in the session, other items outside the session and other sessions. In the experiments on two datasets, Yoochoose1/4 and Diginetica, the recommendation effect of HGNNA is higher than that of other relevant methods, especially in the P@20, which is improved by 0.69 and 1.40 respectively.