Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–26, 2023, Zhengzhou, China

Research Article

Based on Ontology Construction for Personalized Learning Resource Recommendation Research

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  • @INPROCEEDINGS{10.4108/eai.24-11-2023.2343624,
        author={Jiarui  Zhang and Kun  Nie and Hong  Li},
        title={Based on Ontology Construction for Personalized Learning Resource Recommendation Research},
        proceedings={Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24--26, 2023, Zhengzhou, China},
        publisher={EAI},
        proceedings_a={ITEI},
        year={2024},
        month={4},
        keywords={domain ontology; learner modeling; learning resource modeling; personalized recommendation},
        doi={10.4108/eai.24-11-2023.2343624}
    }
    
  • Jiarui Zhang
    Kun Nie
    Hong Li
    Year: 2024
    Based on Ontology Construction for Personalized Learning Resource Recommendation Research
    ITEI
    EAI
    DOI: 10.4108/eai.24-11-2023.2343624
Jiarui Zhang1, Kun Nie1, Hong Li1,*
  • 1: Yunnan Normal University
*Contact email: 651035658@qq.com

Abstract

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.