Recommender System Explanations

Recommender System Explanations is a computational framework that provides transparent, human-understandable justifications for recommendations generated by educational recommender systems by revealing the underlying reasoning, criteria, or data influencing the suggested learning resources, paths, or interventions.

230

Publications

18.7K

Citations

635

Authors

252

Institutions

Publications per year

2017–2026

168

Authors

635

  1. YZ
    Yongfeng Zhang

    Rutgers, The State University of New Jersey, United States

    13 publications · 1.1K citations

    11

    h-index

  2. MZ
    Markus Zanker

    University of Klagenfurt, Austria

    7 publications · 486 citations

    7

    h-index

  3. LC
    Li Chen

    Hong Kong Baptist University, Hong Kong SAR China

    7 publications · 502 citations

    7

    h-index

  4. MG
    Mouadh Guesmi

    University of Duisburg-Essen, Germany

    7 publications · 167 citations

    7

    h-index

  5. JO
    John O’Donovan

    University of California, Santa Barbara, United States

    7 publications · 742 citations

    7

    h-index

1–5 of 635

Venues

  1. arXiv (Cornell University)

    Repository · 14 publications · 326 citations

    10

    h-index

  2. Knowledge-Based Systems

    6 publications · 479 citations

    6

    h-index

  3. ACM Transactions on Interactive Intelligent Systems

    TIIS · 5 publications · 166 citations

    5

    h-index

  4. Information Sciences

    5 publications · 129 citations

    5

    h-index

  5. Proceedings of the ACM Web Conference 2022

    Conference · 4 publications · 83 citations

    4

    h-index

1–5