Implicit Matrix Factorization

Implicit Matrix Factorization is a matrix factorization method that models user-item interactions by treating observed user feedback as implicit indicators of preference, differentiating itself from explicit feedback approaches through its reliance on non-binary, often binary or count-based, interaction signals rather than rated evaluations.

33

Publications

2.4K

Citations

86

Authors

61

Institutions

Publications per year

2017–2026

13

Authors

86

  1. CK
    C Knight

    Royal Brompton Hospital, United Kingdom

    2 publications · 37 citations

    2

    h-index

  2. DC
    D. CHAUSSEE

    Ames Research Center, United States

    2 publications · 1.2K citations

    2

    h-index

  3. DC
    D. CHOI

    Textron (United States), United States

    2 publications · 37 citations

    2

    h-index

  4. TH
    Thomas H. Pulliam

    Ames Research Center, United States

    2 publications · 1.2K citations

    2

    h-index

  5. RD
    Roger Donaldson

    Caspian University, Kazakhstan

    1 publications · 47 citations

    1

    h-index

1–5 of 86

Venues

  1. Journal of Computational Physics

    2 publications · 1.2K citations

    2

    h-index

  2. AIAA Journal

    2 publications · 112 citations

    2

    h-index

  3. Automatica

    1 publications · 44 citations

    1

    h-index

  4. Proceedings of the VLDB Endowment

    PVLDB · 1 publications · 84 citations

    1

    h-index

  5. BIT Numerical Mathematics

    1 publications · 16 citations

    1

    h-index

1–5