Graph Contrastive Learning with Adaptive Augmentation for Recommendation

Graph Convolutional Network (GCN) has been one of the most popular technologies in recommender systems, as it can effectively model high-order relationships. However, these methods usually suffer from two problems: sparse supervision signal and noisy interactions. To address these problems, graph contrastive learning is applied for GCN-based recommendation. The general framework of graph contrastive learning is first to perform data augmentation on the input graph to get two graph views and then maximize the agreement of representations in these views. Despite the effectiveness, existing methods ignore the differences in the impact of nodes and edges when performing data augmentation, which will degrade the quality of the learned representations. Meanwhile, they usually adopt manual data augmentation schemes, limiting the generalization of models. We argue that the data augmentation scheme should be learnable and adaptive to the inherent patterns in the graph structure. Thus, the model can learn representations that remain invariant to perturbations of unimportant structures while demanding fewer resources. In this work, we propose a novel Graph Contrastive learning framework with Adaptive data augmentation for Recommendation (GCARec). Specifically, for adaptive augmentation, we first calculate the retaining probability of each edge based on the attention mechanism and then sample edges according to the probability with a Gumbel Softmax. In addition, the adaptive data augmentation scheme is based on the neural network and requires no domain knowledge, making it learnable and generalizable. Extensive experiments on three real-world datasets show that GCARec outperforms state-of-the-art baselines.