Logit Averaging: Capturing Global Relation for Session-Based Recommendation

Abstract

Session-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating global relationships between sessions. It does not guarantee a better performance because item embeddings learned by solely utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session ones. Some existing methods tried to enhance recommendation performance by adopting memory modules and global transition graphs; however, those need more computation cost and time. We propose a novel algorithm called Logit Averaging (LA), utilizing both (i) local-level logits, which come from intra-sessions item transitions and (ii) global-level logits, which come from gathered logits of related sessions. The proposed method shows an improvement in recommendation performance in respect of accuracy and diversity through extensive experiments.

Keywords

• session-based recommendation
• global relation
• long-tail distribution
• Logit Averaging