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.

翻译：基于知识图谱的推荐方法在推荐系统领域取得了显著成功。然而，对高质量知识图谱的过度依赖成为此类方法的性能瓶颈。具体而言，知识图谱实体的长尾分布以及现实世界中的噪声问题，会导致物品-实体依赖关系偏离真实特征，进而严重影响用户偏好建模的性能。作为一种用于数据增强与去噪的新方法，对比学习为填补这一研究空白提供了思路。但现有工作仅聚焦于物品点击数量的长尾特性，而忽略了用户总点击次数的长尾特征同样可能影响推荐模型性能。为此，受无监督句子表示去偏对比学习（DCLR）启发，我们提出双层去偏对比图学习（TDCGL）模型。具体地，我们设计了双层去偏对比学习（TDCL）并将其部署于知识图谱中，该学习不仅作用于用户-物品对，还作用于用户-用户对以建模高阶关系。此外，为降低对比学习中随机采样带来的偏差，除随机采样获得的负样本外，我们额外添加基于噪声生成的否定样本以确保空间均匀性。在开源数据集上的大量实验表明，我们的方法具有优异的抗噪能力，且显著优于现有最优基线方法。同时，我们还针对TDCL各层必要性开展了消融研究。