Based on Ontology Construction for Personalized Learning Resource Recommendation Research

With the continuous development of information technology, a large number of information resources flood the Internet, and users face the challenge of finding useful resources in the ocean of knowledge. Therefore, building resource repositories that meet users' personalized search needs and providing personalized resource recommendations has become a research hotspot. This paper proposes a personalized learning resource recommendation method based on ontology construction technology and personalized recommendation algorithms. Firstly, domain knowledge, learning resources, and learners are modeled using ontology construction technology. Then, based on a user's historical learning behavior and interests, personalized recommendation scores are calculated, and resources with high scores are selected for recommendation to improve the accuracy and precision of recommendations.