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DORIS: Personalized course recommendation system based on deep learning

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  • Yinping Ma
  • Rongbin Ouyang
  • Xinzheng Long
  • Zhitong Gao
  • Tianping Lai
  • Chun Fan

Abstract

Course recommendation aims at finding proper and attractive courses from massive candidates for students based on their needs, and it plays a significant role in the curricula-variable system. However, nearly all students nowadays need help selecting appropriate courses from abundant ones. The emergence and application of personalized course recommendations can release students from that cognitive overload problem. However, it still needs to mature and improve its scalability, sparsity, and cold start problems resulting in poor quality recommendations. Therefore, this paper proposes a novel personalized course recommendation system based on deep factorization machine (DeepFM), namely Deep PersOnalized couRse RecommendatIon System (DORIS), which selects the most appropriate courses for students according to their basic information, interests and the details of all courses. The experimental results illustrate that our proposed method outperforms other approaches.

Suggested Citation

  • Yinping Ma & Rongbin Ouyang & Xinzheng Long & Zhitong Gao & Tianping Lai & Chun Fan, 2023. "DORIS: Personalized course recommendation system based on deep learning," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-14, June.
    • Handle: RePEc:plo:pone00:0284687
    DOI: 10.1371/journal.pone.0284687
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    References listed on IDEAS

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    1. Lei Fu & XiaoMing Ma & Wei Wang, 2021. "An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering," Complexity, Hindawi, vol. 2021, pages 1-11, May.
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