Analysis of recommendation systems based on knowledge graphs

Due to the rapid development of internet technology, recommendation systems have played a crucial role in improving user experience and enhancing user retention. Knowledge graphs (KG), a technique capable of capturing complex semantic relationships and contextual information, are gradually included in recommendation systems to augment their accuracy and intelligence. This paper reviews the application of knowledge graphs in recommendation systems, analyzing their unique advantages in handling user-item relationships. This paper comprehensively analyzes embedding methods based on tensor decomposition and translation, which primarily designed for static knowledge graphs. This study thoroughly explains the advantages and disadvantages of these methods in practical application. Moreover, this paper discusses the challenges faced by dynamic knowledge graphs, such as temporal data processing, real-time updates, and inference, proposing potential solutions and future research areas. Integrating knowledge graphs with machine learning techniques enhances the ability of social media recommendation systems to understand user preferences and deliver highly personalized recommendations effectively. This study provides theoretical support and practical guidance for the implementing of knowledge graphs in recommendation systems, holding significant academic value and practical significance.