TDCGL: 面向推荐的两级去偏对比图学习
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation
AI总结：
针对知识图谱推荐中实体长尾、噪声及用户点击数长尾影响性能的问题，提出两级去偏对比图学习模型TDCGL，通过用户-物品、用户-用户双级去偏对比及噪声负样本生成提升抗噪性，性能优于现有最优基线。
摘要：
基于知识图谱的推荐方法已在推荐系统领域取得巨大成功。然而，过度依赖高质量知识图谱是这类方法的瓶颈。具体而言，现实世界中知识图谱实体的长尾分布与噪声问题，会导致物品-实体依赖关系偏离对真实特征的反映，严重损害用户偏好建模的性能。对比学习作为一种用于数据增强和去噪的新方法，为填补这一研究空白提供了思路。但主流工作仅关注点击物品数量的长尾特性，却忽略了每个用户总点击次数的长尾特性也可能影响推荐模型的性能。因此，为解决这些问题，受无监督句子表示去偏对比学习（Debiased Contrastive Learning of Unsupervised Sentence Representations, DCLR）的启发，我们提出了两级去偏对比图学习（Two-Level Debiased Contrastive Graph Learning, TDCGL）模型。具体来说，我们设计了两级去偏对比学习（Two-Level Debiased Contrastive Learning, TDCL）并将其部署在知识图谱中，该学习过程不仅在用户-物品对上执行，还在用户-用户对上执行以建模高阶关系。此外，为减少对比学习中随机采样带来的偏差，除了通过随机采样获得的负样本外，我们还添加了基于噪声的负样本生成方式以保证空间均匀性。在开源数据集上开展的大量实验表明，我们的方法具备出色的抗噪声能力，且性能显著优于现有最优基线模型。此外，我们还针对TDCL每一级的必要性开展了消融研究。
英文摘要：
knowledge graph-based recommendation methods have achieved great success in the field of recommender systems. However, over-reliance on high-quality knowledge graphs is a bottleneck for such methods. Specifically, the long-tailed distribution of entities of KG and noise issues in the real world will make item-entity dependent relations deviate from reflecting true characteristics and significantly harm the performance of modeling user preference. Contrastive learning, as a novel method that is employed for data augmentation and denoising, provides inspiration to fill this research gap. However, the mainstream work only focuses on the long-tail properties of the number of items clicked, while ignoring that the long-tail properties of total number of clicks per user may also affect the performance of the recommendation model. Therefore, to tackle these problems, motivated by the Debiased Contrastive Learning of Unsupervised Sentence Representations (DCLR), we propose Two-Level Debiased Contrastive Graph Learning (TDCGL) model. Specifically, we design the Two-Level Debiased Contrastive Learning (TDCL) and deploy it in the KG, which is conducted not only on User-Item pairs but also on User-User pairs for modeling higher-order relations. Also, to reduce the bias caused by random sampling in contrastive learning, with the exception of the negative samples obtained by random sampling, we add a noise-based generation of negation to ensure spatial uniformity. Considerable experiments on open-source datasets demonstrate that our method has excellent anti-noise capability and significantly outperforms state-of-the-art baselines. In addition, ablation studies about the necessity for each level of TDCL are conducted.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
