Item Cluster-aware Prompt Learning for Session-based Recommendation
<|reference_start|>Item Cluster-aware Prompt Learning for Session-based Recommendation: 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.<|reference_end|>
@article{yang2024item,
    title={Item Cluster-aware Prompt Learning for Session-based Recommendation},
    author={Wooseong Yang, Chen Wang, Zihe Song, Weizhi Zhang, Philip S. Yu},
    journal={arXiv preprint arXiv:2410.04756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04756},
    primaryClass={cs.IR cs.AI cs.LG}
}