Quaternion-based graph contrastive learning for recommendation

Graph Convolution Network (GCN) has been applied in recommendation with various architectures for its representation learning capability in graph-structured data. Despite existing GCN-based recommendation models successfully capturing the user-item interactions, they still suffer from two limitations. On the one hand, they model users and items in the Euclidean space with real-value embeddings, which have high distortion when modeling complex graphs. On the other hand, they have not fully explored contrastive learning for GCN-based recommendations. Simply applying augmentation pairs of the same type may make features less generalizable and lead to sub-optimal performance. To this end, in this paper, we propose a Quaternion-based Graph Contrastive Learning (QGCL) recommendation model. It embeds all users and items into the Quaternion space and performs message propagation with quaternion graph convolution layers. Moreover, we attempt to compose different types of data augmentations for augmented views in graph contrastive learning as an auxiliary task. We evaluate the proposed model using three public datasets, and experimental results demonstrate significant improvements over the state-of-the-art methods by a large margin.