通过多通道去噪图建模，提升会话推荐准确性
GraphFusionSBR: Denoising Multi-Channel Graphs for Session-Based Recommendation
- 构建知识图、超图和线图三通道，融合多源信息
- 自适应去除知识图冗余边，降低物品主导影响
- 适合电商与多媒体场景的会话推荐系统研究者
会话推荐系统需从用户会话中捕捉隐式意图，但现有模型存在物品交互主导和会话噪声问题。本文提出多通道推荐模型，包含知识图通道、会话超图通道和会话线图通道，以融合多源信息。模型自适应移除知识图中的冗余边以降噪，并让知识图表示与超图表示协作预测，缓解物品主导现象。同时生成会话内注意力用于去噪，最后以最大化超图与线图通道间互信息作为辅助任务。实验表明，该方法在电商和多媒体推荐中均提升了准确率。代码已开源。
原文摘要 · Abstract (English)
Session-based recommendation systems must capture implicit user intents from sessions. However, existing models suffer from issues such as item interaction dominance and noisy sessions. We propose a multi-channel recommendation model, including a knowledge graph channel, a session hypergraph channel, and a session line graph channel, to capture information from multiple sources. Our model adaptively removes redundant edges in the knowledge graph channel to reduce noise. Knowledge graph representations cooperate with hypergraph representations for prediction to alleviate item dominance. We also generate in-session attention for denoising. Finally, we maximize mutual information between the hypergraph and line graph channels as an auxiliary task. Experiments demonstrate that our method enhances the accuracy of various recommendations, including e-commerce and multimedia recommendations. We release the code on GitHub for reproducibility.\footnote{https://github.com/hohehohe0509/DSR-HK}
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