Knowledge Graphs (KGs) have emerged as invaluable resources for enriching recommendation systems by providing a wealth of factual information and capturing semantic relationships among items. Leveraging KGs can significantly enhance recommendation performance. However, not all relations within a KG are equally relevant or beneficial for the target recommendation task. In fact, certain item-entity connections may introduce noise or lack informative value, thus potentially misleading our understanding of user preferences. To bridge this research gap, we propose a novel knowledge graph diffusion model for recommendation, referred to as DiffKG. Our framework integrates a generative diffusion model with a data augmentation paradigm, enabling robust knowledge graph representation learning. This integration facilitates a better alignment between knowledge-aware item semantics and collaborative relation modeling. Moreover, we introduce a collaborative knowledge graph convolution mechanism that incorporates collaborative signals reflecting user-item interaction patterns, guiding the knowledge graph diffusion process. We conduct extensive experiments on three publicly available datasets, consistently demonstrating the superiority of our DiffKG compared to various competitive baselines. We provide the source code repository of our proposed DiffKG model at the following link: https://github.com/HKUDS/DiffKG.

翻译：知识图谱（KGs）通过提供丰富的实体事实信息并捕捉物品间的语义关联，已成为增强推荐系统的重要资源。利用知识图谱能够显著提升推荐性能。然而，知识图谱中并非所有关系都与目标推荐任务具有同等相关性或有益性。事实上，某些物品-实体连接可能引入噪声或缺乏信息价值，从而可能误导我们对用户偏好的理解。为弥补这一研究空白，我们提出一种全新的推荐知识图谱扩散模型，称为DiffKG。该框架将生成式扩散模型与数据增强范式相结合，实现了鲁棒的知识图谱表示学习。这种整合有助于更好地实现知识感知物品语义与协同关系建模之间的对齐。此外，我们还引入一种协同知识图谱卷积机制，该机制融入反映用户-物品交互模式的协同信号，以引导知识图谱扩散过程。我们在三个公开数据集上开展了大量实验，结果一致表明，相较于多种强基线模型，DiffKG具有显著优势。我们在以下链接提供所提出的DiffKG模型的源代码仓库：https://github.com/HKUDS/DiffKG。