Personalized Learning Recommendation Method Based on Knowledge Graph

Abstract

The advancement of technology has driven the development of contemporary education, and people's learning methods have entered a new era. Fragmented information learning has become mainstream. Effectively organizing this knowledge, and recommending it to learners in an orderly and visual form, is currently a particularly important research work in the field of education. This article focuses on the research of learning path recommendations for educational knowledge graphs. Taking teaching courses as an example, the data pattern of knowledge graphs is defined. Firstly, data processing is completed through data strangeness. Secondly, the Transformer algorithm is used to extract features. A personalized recommendation algorithm based on an attention mechanism is established to achieve group optimization and individual optimization. Finally, deep recommendations and mixed recommendations of learning paths are completed. The simulation model was used for testing and the results were good.