sis 18: e43

Research Article

Design of resource matching model of intelligent education system based on machine learning

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  • @ARTICLE{10.4108/eai.10-2-2022.173381,
        author={Chun-zhi Xiang and Ning-xian Fu and Thippa Reddy Gadekallu},
        title={Design of resource matching model of intelligent education system based on machine learning},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={2},
        keywords={Machine learning, Intelligent education system, Resource matching, K-means clustering},
        doi={10.4108/eai.10-2-2022.173381}
    }
    
  • Chun-zhi Xiang
    Ning-xian Fu
    Thippa Reddy Gadekallu
    Year: 2022
    Design of resource matching model of intelligent education system based on machine learning
    SIS
    EAI
    DOI: 10.4108/eai.10-2-2022.173381
Chun-zhi Xiang1, Ning-xian Fu1, Thippa Reddy Gadekallu2,*
  • 1: College of Information Engineering, The Open University of Henan, Zhengzhou 450008, China
  • 2: School of Information Technology & Engineering, Vellore Institute of Technology, Tamil Nadu, India
*Contact email: thippareddy.g@vit.ac.in

Abstract

Aiming at the problems of cold start and data sparsity in the process of traditional education resource matching, a resource matching model based on machine learning is designed to get the best resource matching result of a better intelligent education system. Firstly, the similarity hierarchical weighting method is used to calculate the user and resource feature similarity by K-means. Then, the target resources and the nearest neighbor are predicted, and the resources with the highest score to match the target user can be selected according to the nearest neighbor score results. The test results show that the recall rate and coverage rate of the matching results of this model are higher than 98% and 96%, which proves that this model can effectively improve the problems of cold start of resource matching and data sparsity.