Using personalized next session to improve session-based recommender systems

In e-commerce, the session-based personalized recommendation remains challenging due to the limited user information within a single session. Merely relying on a user's local data is insufficient. It is vital to consider global data, extracting insights from sessions across all users to glean collaborative information. However, using all session information will waste computing resources. Moreover, much of the global data may not be pertinent to the current user, thereby undermining the quality of recommendations. To address this, we introduce the concept of personalized next session (PNS), selectively referencing sessions most relevant to the user to enhance the limited local data. This work is the first to adopt a deep network architecture study that incorporates the concept of PNS to recommend the next item for a user in the current session. We evaluated our approach on several real-world datasets, and the results show that our model outperforms state-of-the-art recommendation methods.