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arXiv · 2410.04756

Item Cluster-aware Prompt Learning for Session-based Recommendation

Wooseong Yang·Chen Wang·Zihe Song·Weizhi Zhang·Philip S. Yu
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Abstract

Session-based recommendation (SBR) aims to capture dynamic user preferences by analyzing item sequences within individual sessions. However, most existing approaches focus mainly on intra-session item relationships, neglecting the connections between items across different sessions (inter-session relationships), which limits their ability to fully capture complex item interactions. While some methods incorporate inter-session information, they often suffer from high computational costs, leading to longer training times and reduced efficiency. To address these challenges, we propose the CLIP-SBR (Cluster-aware Item Prompt learning for Session-Based Recommendation) framework. CLIP-SBR is composed of two modules: 1) an item relationship mining module that builds a global graph to effectively model both intra- and inter-session relationships, and 2) an item cluster-aware prompt learning module that uses soft prompts to integrate these relationships into SBR models efficiently. We evaluate CLIP-SBR across eight SBR models and three benchmark datasets, consistently demonstrating improved recommendation performance and establishing CLIP-SBR as a robust solution for session-based recommendation tasks.

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Wooseong Yang, Chen Wang, Zihe Song, Weizhi Zhang, Philip S. Yu. 2024-10-07. Item Cluster-aware Prompt Learning for Session-based Recommendation. https://arxiv.org/abs/2410.04756

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