Implicit Session Contexts for Next-Item Recommendations

TL;DR

This paper introduces ISCON, a novel method for implicitly inferring session contexts in session-based recommender systems, significantly improving next-item prediction accuracy by leveraging graph embeddings and clustering.

Contribution

ISCON proposes a new approach to implicitly generate and utilize session contexts, enhancing recommendation performance without explicit context data.

Findings

ISCON outperforms state-of-the-art models in next-item prediction accuracy.

Session contexts inferred by ISCON are unique and meaningful.

The method is effective across multiple datasets.

Abstract

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.