A Review of Knowledge Graph Recommendation Systems Based on VOSviewer

In the era of artificial intelligence, how recommendation algorithms can accurately and effectively solve the problem of matching individuals' interests and needs in the data ocean, so that they can quickly obtain the required information, has been a hot research topic in recent years. With the help of VOSviewer, this paper makes a visual analysis of the relevant core literature in the past five years, and summarizes the four main research directions currently: (1)Improving the performance of recommendation systems through graph embedding technology.(2)Explored knowledge graph perception propagation methods from the perspective of attention mechanism to improve recommendation quality.(3)The widespread application of technologies such as deep learning in knowledge graph recommendation systems has improved recommendation performance.(4)Innovate multi task frameworks to enhance recommendation effectiveness. Finally, summarize its application classification, corresponding technological development, and academic achievements, and propose future technological development directions for recommendation systems.