Explainable Matrix Factorization

Explainable Matrix Factorization is a matrix factorization method that, in contrast to conventional approaches, explicitly incorporates interpretability mechanisms to reveal the latent factors and their contributions in a human-understandable manner, thereby enabling transparent explanations for the derived low-rank representations.

13

Publications

645

Citations

28

Authors

25

Institutions

Publications per year

2017–2026

11

Authors

28

  1. ON
    Olfa Nasraoui

    University of Louisville, United States

    2 publications · 206 citations

    2

    h-index

  2. RK
    Russell Kunes

    Columbia University, United States

    2 publications · 98 citations

    2

    h-index

  3. TN
    Tal Nawy

    Memorial Sloan Kettering Cancer Center, United States

    2 publications · 98 citations

    2

    h-index

  4. BA
    Behnoush Abdollahi

    University of Louisville, United States

    2 publications · 206 citations

    2

    h-index

  5. DP
    Dana Pe’er

    Memorial Sloan Kettering Cancer Center, United States

    2 publications · 98 citations

    2

    h-index

1–5 of 28

Venues

  1. IEEE Access

    2 publications · 119 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 65 citations

    2

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 1 publications · 10 citations

    1

    h-index

  4. Nature Biotechnology

    1 publications · 88 citations

    1

    h-index

  5. IEEE Signal Processing Magazine

    1 publications · 56 citations

    1

    h-index

1–5