用双曲空间增强推荐模型，避免偏好偏移。
Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation
- 引入洛伦兹知识聚合机制捕捉层级结构
- 提出三种模型级增强方法，提升推荐准确率最高达11.03%
- 适合需要建模复杂用户行为的推荐系统研究者
得益于图神经网络（GNNs）和对比学习的有效性，基于GNN的对比学习已成为知识感知推荐的主流方法。然而，现有方法难以有效捕捉用户-物品二分图和知识图谱中的潜在层级结构，且通常通过扰动图结构生成正样本，可能导致用户偏好学习发生偏移。为此，我们提出一种基于模型增强的双曲对比学习方法。首先设计新颖的洛伦兹知识聚合机制，更有效地表示用户与物品；随后提出三种模型级增强技术，辅助双曲对比学习。不同于传统的结构级增强（如边删除），所提方法可避免增强前后正样本对间的偏好偏移。大量实验表明，该方法相较基线模型最大提升达11.03%。
原文摘要 · Abstract (English)
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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