  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.
