Implicit Session Contexts for Next-Item Recommendations
This work addresses the challenge of enhancing recommendation accuracy in session-based systems by implicitly modeling user intents, representing an incremental improvement over existing methods.
The paper tackles the problem of inferring implicit session contexts in session-based recommender systems, where explicit contexts are unavailable, and proposes ISCON to generate and use these contexts for improved next-item recommendations, achieving superior prediction accuracy on four datasets.
Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose ISCON, which implicitly contextualizes sessions. ISCON first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. ISCON then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that ISCON has superior next-item prediction accuracy than state-of-the-art models. A case study of ISCON on the Reddit dataset confirms that assigned session contexts are unique and meaningful.