Multi-intent-aware Session-based Recommendation

TL;DR

This paper introduces MiaSRec, a novel session-based recommendation model that captures multiple user intents within sessions, significantly improving prediction accuracy especially for longer sessions.

Contribution

MiaSRec is the first model to incorporate multiple session representations and frequency embeddings to model diverse user intents in session-based recommendation.

Findings

Outperforms state-of-the-art models on six datasets.

Achieves up to 6.27% improvement in MRR@20.

Achieves up to 24.56% improvement in Recall@20.

Abstract

Session-based recommendation (SBR) aims to predict the following item a user will interact with during an ongoing session. Most existing SBR models focus on designing sophisticated neural-based encoders to learn a session representation, capturing the relationship among session items. However, they tend to focus on the last item, neglecting diverse user intents that may exist within a session. This limitation leads to significant performance drops, especially for longer sessions. To address this issue, we propose a novel SBR model, called Multi-intent-aware Session-based Recommendation Model (MiaSRec). It adopts frequency embedding vectors indicating the item frequency in session to enhance the information about repeated items. MiaSRec represents various user intents by deriving multiple session representations centered on each item and dynamically selecting the important ones. Extensive experimental results show that MiaSRec outperforms existing state-of-the-art SBR models on six datasets, particularly those with longer average session length, achieving up to 6.27% and 24.56% gains for MRR@20 and Recall@20. Our code is available at https://github.com/jin530/MiaSRec.