Hyperbolic Contrastive Learning with Second Order Sampling for Collaborative Filtering

Recently, hyperbolic spaces have proven beneficial for service recommendation due to their exponentially growing spatial properties conforming to power-law distributed user-item networks. Among them, the combination of hyperbolic space with graph convolution has achieved great success. However, hyperbolic convolutional models still perform multi-layer convolution in tangent space (Euclidean space), leading to the still inevitable problem of over-smoothing arising from multi-layer convolution. In addition, most of these models randomly draw negative samples from items that users have not interacted with, so that some of the samples obtained may not be well suited for model optimization. To tackle the above challenges, we propose a new Hyperbolic GCN model based on Contrastive Learning and Second-order Reachable sampling for collaborative filtering (HG-CLSR), which improve high quality of representations by exploring the distribution of users and items in hyperbolic space. Specifically, We first introduce a root alignment approach to encourage embeddings to align with the tangent space, thereby reducing distortions during the embedding mapping process in space. Then, we perform contrastive learning in hyperbolic space to motivate the spatial distribution of nodes to better fit the hyperbolic space. Moreover, we sample in the user’s second-order reachable item set, which ensures that the negative sample is more similar to the positive sample, so that the negative node can provide better information for guiding model optimization. Extensive experiments on three real-world datasets demonstrate that the HG-CLSR is significantly superior compared to existing hyperbolic models.