Explainable Recommendation

Explainable Recommendation is a type of recommendation system that, as a subclass of personalized information filtering methods, differentiates itself by providing transparent and human-interpretable justifications for its suggested items, thereby enhancing user trust and decision-making.

411

Publications

33.4K

Citations

1.2K

Authors

394

Institutions

Publications per year

2017–2026

321

Authors

1.2K

  1. YZ
    Yongfeng Zhang

    Rutgers, The State University of New Jersey, United States

    27 publications · 2.1K citations

    17

    h-index

  2. LC
    Li Chen

    Hong Kong Baptist University, Hong Kong SAR China

    16 publications · 1.4K citations

    15

    h-index

  3. ZF
    Zuohui Fu

    Rutgers, The State University of New Jersey, United States

    11 publications · 521 citations

    10

    h-index

  4. XX
    Xing Xie

    Microsoft Research Asia (China), China

    8 publications · 706 citations

    8

    h-index

  5. BS
    Barry Smyth

    University College Dublin, Ireland

    7 publications · 532 citations

    7

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 28 publications · 856 citations

    16

    h-index

  2. Knowledge-Based Systems

    12 publications · 754 citations

    12

    h-index

  3. ACM Transactions on Information Systems

    9 publications · 503 citations

    9

    h-index

  4. Information Sciences

    7 publications · 215 citations

    7

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 1.1K citations

    7

    h-index

1–5