Abstract: To address the challenges of high-order modeling and insufficient user feature modeling in knowledge graph-based recommendation systems, a diffusion map recommendation model based on multi-hop mechanism (MultiHop-GDN) is proposed. This model mines high-order semantic information from the knowledge graph through an end-to-end method, covering three parts: knowledge graph construction, feature extraction network design and multi-hop diffusion model development. The knowledge graph is constructed using user and project attributes, enabling in-depth analysis of information such as user interests, preferences, and historical behaviors to build user portraits and interest models. A feature extraction network is introduced to capture deep semantic information and obtain prediction values through the calculation of this model. Comparative experiments on two public datasets show that MultiHop-GDN effectively achieves high-level modeling of both users and projects, outperforming other representative models in recommendation effects.

Key words: knowledge graph, recommender system, multi-hop mechanism, diffusion model, deep learning