SACL: Siamese Adaptive Contrastive Learning for Recommendation

Graph neural networks (GNNs) become popular in recommender systems treating the interaction data of user and item as a bipartite graph. Recently, graph contrastive learning achieves superior results for collaborative filtering by reinforcing the learned representations by generating contrastive views through data augmentation. Despite their successful application in recommendation scenarios, there is still some room for improvement: most of these methods perform data augmentation from the data perspective, and the model potential is not exploited enough because more contrastive perspectives are not considered; negative sample bias caused by the different degrees of nodes exists in the contrastive loss. In this paper, we propose a Siamese Adaptive Contrastive learning framework (SACL) to mitigate these issues. Our model utilizes Siamese network as a small perturbation to the model and combines it with data augmentation to learn more robust representations and realizes adaptive contrastive learning introducing the common neighbors' information of users and items to weight negative samples. Experiments on several public datasets show better performance of our model compared to existing representative methods.