University Course Recommendation Based on Multi-source Educational Knowledge Graph

In response to the course recommendation problem in the undergraduate teaching scenario of higher education, a recommendation algorithm based on a multi-source educational knowledge graph is proposed. Firstly, the course correlation is analyzed, and relevant data is extracted from various databases of the university for preprocessing. Then, a multi-path RNN encoding method is proposed to embed multi-source information into the educational knowledge graph. Subsequently, an MLP is utilized to model the interaction between students and courses, aiming to predict student course selections. Finally, a comparative experiment is conducted to validate the feasibility of the university recommendation method based on the multi-source educational knowledge graph.