  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.
