Design and Implementation of an Electronic Journal Resource Recommendation System Integrating User Profile and Knowledge Graph

In order to facilitate users to find the literature they need in a massive number of electronic journals, this article designs and implements a recommendation system based on the fusion of user profile and knowledge graph recommendation model. The system extracts feature from users downloaded electronic journal resources, and combines the knowledge graph of electronic journal resources to construct a recommendation model that effectively alleviates the problem of data sparsity and enhances the recommendation effect. System testing shows that the system has implemented the functions of each module and can provide personalized recommendations to users.