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knowledge graph-based recommendation methods have achieved great success inthe field of recommender systems. However, over-reliance on high-qualityknowledge graphs is a bottleneck for such methods. Specifically, thelong-tailed distribution of entities of KG and noise issues in the real worldwill make item-entity dependent relations deviate from reflecting truecharacteristics and significantly harm the performance of modeling userpreference. Contrastive learning, as a novel method that is employed for dataaugmentation and denoising, provides inspiration to fill this research gap.However, the mainstream work only focuses on the long-tail properties of thenumber of items clicked, while ignoring that the long-tail properties of totalnumber of clicks per user may also affect the performance of the recommendationmodel. Therefore, to tackle these problems, motivated by the DebiasedContrastive Learning of Unsupervised Sentence Representations (DCLR), wepropose Two-Level Debiased Contrastive Graph Learning (TDCGL) model.Specifically, we design the Two-Level Debiased Contrastive Learning (TDCL) anddeploy it in the KG, which is conducted not only on User-Item pairs but also onUser-User pairs for modeling higher-order relations. Also, to reduce the biascaused by random sampling in contrastive learning, with the exception of thenegative samples obtained by random sampling, we add a noise-based generationof negation to ensure spatial uniformity. Considerable experiments onopen-source datasets demonstrate that our method has excellent anti-noisecapability and significantly outperforms state-of-the-art baselines. Inaddition, ablation studies about the necessity for each level of TDCL areconducted.

TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation