EconPapers    
Economics at your fingertips  
 

DORIS: Personalized course recommendation system based on deep learning

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

PLOS ONE, 2023, vol. 18, issue 6, 1-14

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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0284687 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 84687&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0284687

DOI: 10.1371/journal.pone.0284687

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().

 
Page updated 2025-06-07
Handle: RePEc:plo:pone00:0284687