Hyperbolic Graph Contrastive Learning for Collaborative Filtering

Abstract

Hyperbolic space based collaborative filtering has emerged as a popular topic in recommender systems. Compared to the euclidean space, hyperbolic space is more suitable to the tree-like structures in the user-item interactions and can achieve better recommender performance. Although some works have been devoted to this popular topic and made some progresses, they use tangent space as an approximation of hyperbolic space to implement model. Despite the effectiveness, such methods fail to fully exploit the advantages of hyperbolic space and still suffer from the data sparsity issue, which severely limits the recommender performance. To tackle these problems, we refer to the self-supervised learning technique and novelly propose a Hyperbolic Graph Contrastive Learning (HyperCL) framework. Specifically, our framework encodes the augmentation views from both the tangent space and the hyperbolic space, and construct the contrast pairs based on their corresponding learned node representations. Our model not only leverages the geometric advantages of both sides but also achieves seamless information transmission between the two spaces. Extensive experimental results on public benchmark datasets demonstrate that our model is highly competitive and outperforms leading baselines by considerable margins. Further experiments validate the robustness and the superiority of contrastive learning paradigm.