Explainable Recommendation Models

Explainable Recommendation Models is a class of recommender systems that, as a genus, generate personalized suggestions for users, and, as a differentia, explicitly provide interpretable justifications for their recommendations to enhance transparency, trust, and user understanding.

135

Publications

9.6K

Citations

444

Authors

190

Institutions

Publications per year

2017–2026

115

Authors

444

  1. YZ
    Yongfeng Zhang

    Rutgers, The State University of New Jersey, United States

    14 publications · 1.2K citations

    12

    h-index

  2. LW
    Le Wu

    Hefei University of Technology, China

    5 publications · 279 citations

    5

    h-index

  3. LC
    Li Chen

    Hong Kong Baptist University, Hong Kong SAR China

    5 publications · 436 citations

    5

    h-index

  4. EC
    Enhong Chen

    University of Science and Technology of China, China

    4 publications · 163 citations

    4

    h-index

  5. ZF
    Zuohui Fu

    Rutgers, The State University of New Jersey, United States

    4 publications · 147 citations

    4

    h-index

1–5 of 444

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 200 citations

    10

    h-index

  2. Knowledge-Based Systems

    10 publications · 686 citations

    10

    h-index

  3. Information Sciences

    5 publications · 122 citations

    5

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 121 citations

    4

    h-index

  5. World Wide Web

    4 publications · 179 citations

    4

    h-index

1–5