Research Progress of Knowledge Graph and Attention Mechanism in Recommender Systems

With the development of the Internet and the explosive expansion of information volume, it becomes arduous for users to make a choice in the face of overwhelming information. This makes the recommendation system an effective solution for the problem of information overload. Recommendation systems can acquire users' personalized preferences by comprehending their interactive behavior, thereby providing accurate recommendations. However, recommendation systems invariably encounter issues such as data sparsity and cold start. The introduction of auxiliary information can effectively alleviate these problems. Meanwhile, the employment of attention mechanisms can assist the system in more accurately understand user behavior and preference information, thus providing more precise personalized recommendations. In this paper, the research progress of knowledge graphs and attention mechanisms in recommendation systems is explored through top journals and several Chinese papers.