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Mobile Wireless Middleware, Operating Systems and Applications. 9th EAI International Conference, MOBILWARE 2020, Hohhot, China, July 11, 2020, Proceedings

Research Article

Research on the Application of Personalized Course Recommendation of Learn to Rank Based on Knowledge Graph

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  • @INPROCEEDINGS{10.1007/978-3-030-62205-3_2,
        author={Hao Wu and FanJun Meng},
        title={Research on the Application of Personalized Course Recommendation of Learn to Rank Based on Knowledge Graph},
        proceedings={Mobile Wireless Middleware, Operating Systems and Applications. 9th EAI International Conference, MOBILWARE 2020, Hohhot, China, July 11, 2020, Proceedings},
        proceedings_a={MOBILWARE},
        year={2020},
        month={11},
        keywords={Learning to rank Knowledge Graph Personalized recommendation algorithm Interest conversion Node2vec},
        doi={10.1007/978-3-030-62205-3_2}
    }
    
  • Hao Wu
    FanJun Meng
    Year: 2020
    Research on the Application of Personalized Course Recommendation of Learn to Rank Based on Knowledge Graph
    MOBILWARE
    Springer
    DOI: 10.1007/978-3-030-62205-3_2
Hao Wu1,*, FanJun Meng1
  • 1: Inner Mongolia Normal University, Hohhot
*Contact email: 292866851@qq.com

Abstract

Aiming at the problem that the computer technology level of most non computer major students in Colleges and universities is not even, which can not be effectively aimed at teaching, Use the evaluation data of students for each course chapter to integrate the Knowledge Graph, Build a hybrid model of sequencing learning, student user migration and basic characteristics, Finally, the top-N recommended courses are sorted. In general, the recommendation algorithm is only applied to the recommendation service of e-commerce platform, The personalized recommendation algorithm proposed in this paper is mainly used to serve students to improve the quality of course teaching.

Keywords
Learning to rank Knowledge Graph Personalized recommendation algorithm Interest conversion Node2vec
Published
2020-11-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-62205-3_2
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