Graphormer based contrastive learning for recommendation

In recent years, Graph Neural Networks (GNNs) have become a powerful tool for graph representation in recommendation systems. Graphhormer integrates its graph structure into a Transformer to provide state-of-the-art performance for graph prediction tasks. Meanwhile, self-supervised contrastive learning has succeeded in processing highly sparse data. Despite these successes, most graph contrastive methods perform random enhancement, such as node/edge perturbation, on a user-item interaction graph, or perform heuristic enhancement techniques, such as user clustering, to generate a new contrastive view. These methods cannot preserve the inherent semantic structure well and are susceptible to noise. In this paper, we propose a Graphormer-based graph contrastive learning method, GO-GCL, which reduces the influence of noise while retaining the inherent semantic structure to some extent, and improves the versatility and robustness of the recommendation based on contrastive learning. The model is represented by Graphormer and then uses singular value decomposition for contrastive enhancement. The experiments on Yelp datasets show that our model has a significant improvement over existing GCL-based approaches. In-depth analyses show the superiority and robustness of GO-GCL in data sparsity and prevalence bias.