Matrix Factorization

Matrix Factorization is a dimensionality reduction technique in recommender systems that decomposes a user-item interaction matrix into lower-dimensional latent factor matrices to uncover hidden patterns governing user preferences and item characteristics.

11.2K

Publications

867.7K

Citations

18.6K

Authors

3.2K

Institutions

Publications per year

2017–2026

3.8K

Authors

18.6K

  1. AC
    Andrzej Cichocki

    RIKEN Center for Brain Science, Japan

    86 publications · 9K citations

    46

    h-index

  2. XL
    Xin Luo

    Chongqing Institute of Green and Intelligent Technology, China

    65 publications · 6.4K citations

    43

    h-index

  3. LD
    Lieven De Lathauwer

    KU Leuven, Belgium

    44 publications · 10.9K citations

    31

    h-index

  4. ND
    Nicholas D. Sidiropoulos

    University of Minnesota, United States

    43 publications · 5.4K citations

    29

    h-index

  5. DT
    Dacheng Tao

    The University of Sydney, Australia

    41 publications · 5.3K citations

    29

    h-index

1–5 of 18.6K

Venues

  1. SIAM Journal on Matrix Analysis and Applications

    353 publications · 34K citations

    85

    h-index

  2. arXiv (Cornell University)

    Repository · 518 publications · 30K citations

    74

    h-index

  3. Linear Algebra and its Applications

    557 publications · 24K citations

    67

    h-index

  4. IEEE Transactions on Signal Processing

    184 publications · 14.8K citations

    59

    h-index

  5. SIAM Journal on Scientific Computing

    169 publications · 15K citations

    58

    h-index

1–5