Multi-session aware hypergraph neural network for session-based recommendation

Session-based user behavior prediction is a difficulty in network behavior modeling due to the limitation of information. In recent years, the neural network has become a new research direction in recommendation system, however, the existing graph structure recommended method simple binary relation of concern within the session, but in real life tend to have the multiple complex relationships between items. In addition, hyperedges lack displayed position information in hypergraphs, and items in different orders may construct the same hyperedges, which necessarily limits the ability to obtain exact vector representations of sessions. Therefore, to solve the above limitations, a multi-session aware hypergraph neural network (MA-HGNN) for session-based recommendation is proposed, which takes advantage of hypergraphs to model complex multivariate relationships in sessions, and alleviates the hyperedge isomorphism problem by preserving sequence information. At the same time, the co-occurrence graph structure and the local session graph structure are established to realize the connection between the similar user intentions in different sessions and the potential behavior patterns in the same session. Finally, experiments are carried out on three real-world datasets Diginetica, Tmall and Nowplaying, and the models proposed in our work are significantly improved, which proves the effectiveness of the method.