面向知识感知推荐的模型增强双曲对比学习
Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation
AI总结：
针对知识感知推荐中层次结构建模不足及图结构增强导致偏好偏移的问题，提出结合Lorentzian知识聚合与模型级增强的双曲对比学习方法，最高性能提升11.03%。
摘要：
得益于图神经网络（GNN）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，大多数现有基于对比学习的方法难以有效捕捉用户-物品二部图和知识图谱中潜在的层次结构。此外，它们通常通过扰动图结构来生成对比学习的正样本，这可能导致用户偏好学习发生偏移。为克服这些局限，我们提出了面向知识感知推荐的模型增强双曲对比学习。为捕捉内在的层次图结构，我们首先设计了一种新颖的Lorentzian知识聚合机制，从而能够更有效地表示用户和物品。随后，我们提出三种模型级增强技术以辅助双曲对比学习。与经典的结构级增强（例如边丢弃）不同，所提出的模型增强能够避免增强正对之间的偏好偏移。最后，我们开展了大量实验，证明所提方法相较现有基线具有优越性，最大提升达11.03%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
