POEM: Position Order Enhanced Model for Session-based Recommendation Service

Abstract

Session-based recommendation, which aims to predict the next action of an anonymous user base on the interaction information in a session, plays a crucial role in many online services. Recent works solve the problem with the latest deep learning techniques and have achieved good performance on some datasets. However, they have some shortcomings that affect their practical application value: a) the drift process of users' interests in the browsing is not well explored; b) the association between a user's current interests and general preferences in the session is not adequately considered. They mostly assume that the last interaction has a significant impact on the next interaction, which makes them work well only in limited scenarios and specific datasets. To address these limitations, we propose a session-based recommendation model called POEM, which explicitly considers the impact of interaction order relationships on recommendations by emphasizing position attributes in the session. Specifically, POEM models the macro and micro importance of each item in the session, the influence of user interaction order on the item-level collaboration, and the session-level collaboration reflected in the user interest drift process, respectively. Extensive experiments of the effectiveness, efficiency, and universality on three real-world datasets show that our method outperforms various state-of-the-art session-based recommendation methods consistently.