Rethinking Item Importance in Session-based Recommendation

TL;DR

This paper introduces SR-IEM, a session-based recommendation model that uses a modified self-attention mechanism to estimate item importance, effectively capturing user preferences and improving recommendation accuracy.

Contribution

It proposes a novel importance extraction module with a modified self-attention mechanism for session-based recommendation, enhancing prediction of user preferences.

Findings

SR-IEM outperforms state-of-the-art methods in Recall and MRR.

The approach reduces computational complexity.

It effectively combines long-term and recent user behaviors.

Abstract

Session-based recommendation aims to predict users' based on anonymous sessions. Previous work mainly focuses on the transition relationship between items during an ongoing session. They generally fail to pay enough attention to the importance of the items in terms of their relevance to user's main intent. In this paper, we propose a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM, that considers both a user's long-term and recent behavior in an ongoing session. We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user's long-term preference. Item recommendations are produced by combining the user's long-term preference and current interest as conveyed by the last interacted item. Experiments conducted on two benchmark datasets validate that SR-IEM outperforms the start-of-the-art in terms of Recall and MRR and has a reduced computational complexity.