Distributed Matrix Factorization

Distributed Matrix Factorization is a computational framework within the class of matrix factorization methods that is distinguished by its design to partition and process large-scale matrices across multiple computing nodes, enabling scalable and parallelized decomposition through distributed memory and coordinated computation.

76

Publications

3.7K

Citations

237

Authors

119

Institutions

Publications per year

2017–2026

31

Authors

237

  1. RB
    Reza Bosagh Zadeh

    Stanford University, United States

    3 publications · 132 citations

    3

    h-index

  2. SR
    Suju Rajan

    Yahoo (United States), United States

    3 publications · 73 citations

    3

    h-index

  3. NL
    Nathan Liu

    Yahoo (United States), United States

    3 publications · 73 citations

    3

    h-index

  4. SS
    Sebastian Schelter

    University of Amsterdam, Netherlands

    2 publications · 72 citations

    2

    h-index

  5. UK
    U Kang

    Seoul National University, South Korea

    2 publications · 80 citations

    2

    h-index

1–5 of 237

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 233 citations

    7

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 28 citations

    2

    h-index

  3. Systems & Control Letters

    2 publications · 73 citations

    2

    h-index

  4. ACM Transactions on Knowledge Discovery from Data

    TKDD · 2 publications · 23 citations

    2

    h-index

  5. The Journal of Supercomputing

    2 publications · 42 citations

    2

    h-index

1–5