Multi-intent-aware Session-based Recommendation

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.

SIGIR 2024. 5 pages

References in corpus (9)

• Session-based Recommendation with Graph Neural Networks
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
• Global Context Enhanced Graph Neural Networks for Session-based Recommendation
• Evaluation of Session-based Recommendation Algorithms
• Empirical Analysis of Session-Based Recommendation Algorithms
• Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
• Learning Multi-granularity User Intent Unit for Session-based Recommendation
• Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation
• Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation

Cited by in corpus (1)

• Multi-view Intent Learning and Alignment with Large Language Models for Session-based Recommendation