通过多图协同训练，更精准捕捉用户短会话中的真实意图。
Multi-Graph Co-Training for Capturing User Intent in Session-based Recommendation
- 构建当前会话、相似会话与全局物品关系三张图协同学习
- 在Diginetica上达P@20提升2.00%，MRR@20提升10.70%
- 适合处理数据稀疏的会话推荐场景，尤其对短期行为建模有效
会话推荐旨在基于匿名用户会话序列预测用户下一步交互项。该领域面临的主要挑战是因短期交互导致的数据稀疏问题。现有方法多依赖当前会话内信息，忽视了丰富的辅助信息。为此，我们提出多图协同训练模型（MGCOT），不仅利用当前会话图，还结合相似会话图与全局物品关系图，从多视角全面挖掘内在关联，使会话表征相互补充。MGCOT采用多头注意力机制捕捉关键会话意图，并通过对比学习构建准确稳健的会话表示。在三个数据集上的大量实验表明，MGCOT显著提升了会话推荐性能，尤其在Diginetica数据集上，P@20提升2.00%，MRR@20提升10.70%。相关资源已公开于GitHub：https://github.com/liang-tian-tian/MGCOT。
原文摘要 · Abstract (English)
Session-based recommendation focuses on predicting the next item a user will interact with based on sequences of anonymous user sessions. A significant challenge in this field is data sparsity due to the typically short-term interactions. Most existing methods rely heavily on users' current interactions, overlooking the wealth of auxiliary information available. To address this, we propose a novel model, the Multi-Graph Co-Training model (MGCOT), which leverages not only the current session graph but also similar session graphs and a global item relation graph. This approach allows for a more comprehensive exploration of intrinsic relationships and better captures user intent from multiple views, enabling session representations to complement each other. Additionally, MGCOT employs multi-head attention mechanisms to effectively capture relevant session intent and uses contrastive learning to form accurate and robust session representations. Extensive experiments on three datasets demonstrate that MGCOT significantly enhances the performance of session-based recommendations, particularly on the Diginetica dataset, achieving improvements up to 2.00% in P@20 and 10.70% in MRR@20. Resources have been made publicly available in our GitHub repository https://github.com/liang-tian-tian/MGCOT.
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