Privacy-preserving Matrix Factorization

Privacy-Preserving Matrix Factorization is a computational technique within collaborative filtering-based recommendation systems that performs matrix factorization while enforcing privacy constraints to protect sensitive user data during model training and inference.

80

Publications

3.6K

Citations

263

Authors

135

Institutions

Publications per year

2017–2026

63

Authors

263

  1. LY
    Leo Yu Zhang

    Deakin University, Australia

    4 publications · 138 citations

    4

    h-index

  2. YW
    Yong Wang

    Chongqing University of Posts and Telecommunications, China

    3 publications · 114 citations

    3

    h-index

  3. XR
    Xun Ran

    Chongqing University of Posts and Telecommunications, China

    3 publications · 109 citations

    3

    h-index

  4. JM
    Jun Ma

    Lanzhou University of Technology, China

    3 publications · 109 citations

    3

    h-index

  5. JS
    Junbum Shin

    Samsung (South Korea), South Korea

    3 publications · 360 citations

    3

    h-index

1–5 of 263

Venues

  1. Information Sciences

    4 publications · 137 citations

    4

    h-index

  2. Knowledge-Based Systems

    4 publications · 150 citations

    4

    h-index

  3. IEEE Access

    3 publications · 113 citations

    3

    h-index

  4. arXiv (Cornell University)

    Repository · 3 publications · 45 citations

    3

    h-index

  5. IEEE Transactions on Industrial Informatics

    2 publications · 95 citations

    3

    h-index

1–5