Using personalized next session to improve session-based recommender systems

Abstract

In e-commerce, session-based personalized recommendation is still a challenging task due to the limited information of users in a session. Therefore, it is not enough to include only the user's local information. Instead, we should consider global information, i.e., extract information from all users' sessions to explore more collaborative information. However, using all session information will waste computing resources, and there will be a lot of information in the global information that is not relevant to the current user, which will damage the correctness of the prediction. Therefore, instead of referencing all global information, we select only those sessions that are useful to users, the so-called Personalized Next Session (PNS), as global information to improve the lack of local information. This work is the rst to adopt a deep network architecture study incorporating the concept of PNS to recommend the next item of the current session to the user. We have conducted experiments on several real-world datasets. Extensive experimental results show that our proposed deep network architecture outperforms state-of-the-art recommendation methods.