通过聚类提示学习，高效建模会话内外物品关联。
Item Cluster-aware Prompt Learning for Session-based Recommendation
- 构建全局图捕捉会话内/间物品关系
- 用软提示融合关系，提升推荐准确率
- 适配多模型，训练效率高
会话推荐旨在通过分析单个会话内的物品序列捕捉动态用户偏好。然而，现有方法多聚焦会话内物品关系，忽视跨会话的关联（即会话间关系），限制了对复杂物品交互的建模能力。尽管部分方法引入会话间信息，但常伴随高计算开销，导致训练时间长、效率低。为此，我们提出CLIP-SBR框架，包含两个模块：1）物品关系挖掘模块，构建全局图以有效建模会话内与会话间关系；2）物品聚类感知提示学习模块，利用软提示高效将关系整合进会话推荐模型。我们在八个会话推荐模型和三个基准数据集上评估，结果一致显示性能提升，验证了CLIP-SBR在会话推荐任务中的有效性与鲁棒性。
原文摘要 · Abstract (English)
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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