Adaptive Graph Contrastive Learning for Recommendation

Recently, graph neural networks (GNNs) have been successfully applied to recommender systems as an effective collaborative filtering (CF) approach. The key idea of GNN-based recommender system is to recursively perform the message passing along the user-item interaction edge for refining the encoded embeddings, relying on sufficient and high-quality training data. Since user behavior data in practical recommendation scenarios is often noisy and exhibits skewed distribution, some recommendation approaches, e.g., SGL and SimGCL, leverage self-supervised learning to improve user representations against the above issues. Despite their effectiveness, however, they conduct self-supervised learning through creating contrastvie views, depending on the exploration of data augmentations with the problem of tedious trial-and-error selection of augmentation methods. In this paper, we propose a novel Adaptive Graph Contrastive Learning (AdaptiveGCL) framework which conducts graph contrastive learning with two adaptive contrastive view generators to better empower CF paradigm. Specifically, we use two trainable view generators, which are a graph generative model and a graph denoising model respectively, to create contrastive views. Two generators are able to create adaptive contrastive views, addressing the problem of model collapse and achieving adaptive contrastive learning. With two adaptive contrasive views, more additionally high-quality training signals will be introduced into the CF paradigm and help to alleviate the data sparsity and noise issues. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods. Further visual analysis intuitively explains why our AdaptiveGCL outperforms existing contrastive learning approaches based on selected data augmentation methods.