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Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part I

Research Article

Personalized Recommendation Method of Online Career Guidance Curriculum Resources Based on Collaborative Filtering

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-50543-0_1,
        author={Juanjuan Zou},
        title={Personalized Recommendation Method of Online Career Guidance Curriculum Resources Based on Collaborative Filtering},
        proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part I},
        proceedings_a={ADHIP},
        year={2024},
        month={3},
        keywords={Collaborative Filtering Employment Guidance Courses Curriculum Resources Personalized Recommendations},
        doi={10.1007/978-3-031-50543-0_1}
    }
    
  • Juanjuan Zou
    Year: 2024
    Personalized Recommendation Method of Online Career Guidance Curriculum Resources Based on Collaborative Filtering
    ADHIP
    Springer
    DOI: 10.1007/978-3-031-50543-0_1
Juanjuan Zou1,*
  • 1: Chongqing Vocational Institute of Engineering
*Contact email: 17726637816@163.com

Abstract

Aiming at the problems of low recommendation efficiency and inaccurate recommendation results of existing resource recommendation algorithms, this paper proposes a personalized recommendation method design of online career guidance curriculum resources based on collaborative filtering. Firstly, analyze the principle of personalized recommendation of course resources, then establish a user social network model, and calculate the similarity based on user interest preferences and course resource ratings. Finally, based on this, complete the design of personalized recommendation methods for online employment guidance course resources. The feasibility of the proposed method was demonstrated through comparative experiments. The test results showed that the MAE value of the proposed method was between 0.7 and 0.78, the average recommendation time was less than 13.3 ms, and the F-value was higher than 0.95, which is superior to the comparative method. The recommendation efficiency is higher and the recommendation results are more accurate, indicating good application value.

Keywords
Collaborative Filtering Employment Guidance Courses Curriculum Resources Personalized Recommendations
Published
2024-03-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-50543-0_1
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