CurvGCL: Mixed-Curvature Manifolds Graph Contrastive Learning for Knowledge-enhanced Recommendation

Recently, contrastive learning (CL) has gained significant traction in the recommendation field for its ability to extract stable signals from unsupervised data. However, practical applications often face challenges due to sparse and noisy data, limiting the effectiveness of CL in downstream tasks. Although knowledge graphs (KGs) offer valuable auxiliary information in such scenarios, they inherently possess significant non-Euclidean properties. Existing methods that embed data in a single constant curvature manifold can only accommodate specific graph structures, such as linear, hierarchical, or cyclic ones, making it challenging to model diverse and complex geometric topologies effectively. To address these limitations, we design CurvGCL, a novel graph contrastive learning framework that leverages mixed-curvature manifold representations to model complex geometric structures for knowledge-enhanced recommendation. Specifically, we propose a multi-manifolds adaptive fusion mechanism that preserves multiple geometric structures within KGs, enhancing their representation capabilities and allowing the integrated knowledge to adapt flexibly to various non-Euclidean geometric forms. Additionally, we design a novel method for adaptive-enhanced graph structure representation learning based on mixed-curvature manifold spaces, dynamically removing edges that are not relevant to recommendation during data augmentation, thereby generating high-quality views. Extensive experiments on four public datasets demonstrate that CurvGCL outperforms state-of-the-art methods.