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.

本研究提出了基于知识图谱的推荐方法存在对高质量知识图谱的过度依赖问题，并介绍了两级无偏对比图学习（TDCGL）模型，该模型通过引入对比学习和降噪方法，解决了实体分布不均和噪声问题，从而提高推荐模型的性能。