Symmetric Graph Contrastive Learning against Noisy Views for Recommendation

TL;DR

This paper introduces a symmetric graph contrastive learning method that is robust against noisy views, significantly improving recommendation accuracy by reducing the negative impact of poor data augmentation.

Contribution

The paper proposes a novel symmetric contrastive learning framework with theoretical guarantees to mitigate the effects of noisy views in graph-based recommendation systems.

Findings

Substantial accuracy improvements up to 12.25% over competing models.

Theoretical proof of high tolerance to noisy views.

Effective on three real-world datasets.

Abstract

Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consistency between contrastive views. However, existing augmentation methods, such as directly perturbing interaction graph (e.g., node/edge dropout), may interfere with the original connections and generate poor contrasting views, resulting in sub-optimal performance. In this paper, we define the views that share only a small amount of information with the original graph due to poor data augmentation as noisy views (i.e., the last 20% of the views with a cosine similarity value less than 0.1 to the original view). We demonstrate through detailed experiments that noisy views will significantly degrade recommendation performance. Further, we propose a model-agnostic Symmetric Graph Contrastive Learning (SGCL) method with theoretical guarantees to address this issue. Specifically, we introduce symmetry theory into graph contrastive learning, based on which we propose a symmetric form and contrast loss resistant to noisy interference. We provide theoretical proof that our proposed SGCL method has a high tolerance to noisy views. Further demonstration is given by conducting extensive experiments on three real-world datasets. The experimental results demonstrate that our approach substantially increases recommendation accuracy, with relative improvements reaching as high as 12.25% over nine other competing models. These results highlight the efficacy of our method.