Mixed Augmentation Contrastive Learning for Graph Recommendation System

Introducing contrastive learning into graph recommendation can alleviate data sparsity. Graph recommendation systems based on contrastive learning typically employ a single structure augmentation to generate contrastive views. Recent research suggests feature augmentation-adding uniform noise perturbations in the feature space-as a replacement for structure augmentation in contrastive learning. This augmentation can mitigate popularity bias and achieve better recommendation performance than structure augmentation. Graph structure augmentation enables the model to be robust to adversarial samples. Feature augmentation can obtain more uniform feature representations and obtain better performance. Thus, we propose a Mixed Augmentation Contrastive Learning for Recommendation (MACLR). In this paper, we apply graph contrastive learning to recommender systems, where we combine structure augmentation and feature augmentation instead of single augmentation to generate augmented views. Experimental results demonstrate that MACLR effectively integrates the advantages of structure augmentation and feature augmentation, achieving better performance than using a single augmentation method.