Time-Sensitive Collaborative Interest Aware Model for Session-Based Recommendation

Abstract

Session-based recommendation, which aims at predicting user's action based on anonymous sessions, is a challenging problem due to the uncertainty of user's behaviors and limited clicked information. Existing methods model users' interests to relieve the uncertainty of user's behavior prediction. However, most methods mainly focus on the current session, ignoring collaborative information (i.e., collaborative interest) in neighborhood sessions with similar interests. We argue that relying on limited implicit feedbacks within a session is insufficient to precisely infer user's interest, especially in the absence of user's profiles and historical behaviors. This paper proposes a novel model called Time-Sensitive Collaborative Interest Aware (TSCIA) to tackle this problem. It explicitly aggregates similar interests from neighborhood sessions to model the general collaborative interest, and simultaneously takes users' interest drifts into account. Finally, both current session and collaborative information are used for next-item prediction. Extensive experiments on public datasets demonstrate the effectiveness of our model.