Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation

• URL: http://arxiv.org/abs/2505.08157v1
• Date: Tue, 13 May 2025 01:30:27 GMT
• Title: Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation
• Authors: Shengyin Sun, Chen Ma,
• Abstract summary: We propose hyperbolic contrastive learning with model-augmentation for knowledge-aware recommendation.<n>To capture the intrinsic hierarchical graph structures, we first design a novel Lorentzian knowledge aggregation mechanism.<n>Then, we propose three model-level augmentation techniques to assist Hyperbolic contrastive learning.
• Score: 3.7650153431012088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Benefiting from the effectiveness of graph neural networks (GNNs) and contrastive learning, GNN-based contrastive learning has become mainstream for knowledge-aware recommendation. However, most existing contrastive learning-based methods have difficulties in effectively capturing the underlying hierarchical structure within user-item bipartite graphs and knowledge graphs. Moreover, they commonly generate positive samples for contrastive learning by perturbing the graph structure, which may lead to a shift in user preference learning. To overcome these limitations, we propose hyperbolic contrastive learning with model-augmentation for knowledge-aware recommendation. To capture the intrinsic hierarchical graph structures, we first design a novel Lorentzian knowledge aggregation mechanism, which enables more effective representations of users and items. Then, we propose three model-level augmentation techniques to assist Hyperbolic contrastive learning. Different from the classical structure-level augmentation (e.g., edge dropping), the proposed model-augmentations can avoid preference shifts between the augmented positive pair. Finally, we conduct extensive experiments to demonstrate the superiority (maximum improvement of $11.03\%$) of proposed methods over existing baselines.

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