Content-based Learning Resource Recommendation

Content-Based Learning Resource Recommendation is a recommender system approach that generates personalized learning resource suggestions by analyzing the intrinsic features of educational content and matching them to a learner's profile based on prior interactions or stated preferences.

49

Publications

1.8K

Citations

145

Authors

64

Institutions

Publications per year

2017–2026

27

Authors

145

  1. KI
    Khairil Imran Ghauth

    Multimedia University, Malaysia

    3 publications · 243 citations

    3

    h-index

  2. NA
    Nor Aniza Abdullah

    University of Malaya, Malaysia

    3 publications · 243 citations

    3

    h-index

  3. ON
    Olfa Nasraoui

    University of Louisville, United States

    2 publications · 76 citations

    2

    h-index

  4. MM
    Miller M. Mendes

    Universidade Federal de Uberlândia, Brazil

    1 publications · 10 citations

    1

    h-index

  5. JG
    Javier García-Alonso

    Universidade de Vigo, Spain

    1 publications · 20 citations

    1

    h-index

1–5 of 145

Venues

  1. Electronics

    2 publications · 184 citations

    2

    h-index

  2. Applied Sciences

    2 publications · 42 citations

    2

    h-index

  3. IEEE Transactions on Learning Technologies

    2 publications · 25 citations

    2

    h-index

  4. Scientific Programming

    2 publications · 32 citations

    2

    h-index

  5. AI Communications

    AICOM · 1 publications · 18 citations

    1

    h-index

1–5