Probabilistic Matrix Factorization

Probabilistic Matrix Factorization is a probabilistic graphical model for collaborative filtering that factorizes a user-item interaction matrix into latent user and item feature matrices under Gaussian observation noise, distinguishing it from deterministic matrix factorization methods by modeling uncertainty in predictions through a probabilistic likelihood framework.

273

Publications

22.9K

Citations

796

Authors

336

Institutions

Publications per year

2017–2026

113

Authors

796

  1. IK
    Irwin King

    Chinese University of Hong Kong, Hong Kong SAR China

    7 publications · 1.9K citations

    7

    h-index

  2. AB
    Arindam Banerjee

    University of Minnesota, United States

    7 publications · 603 citations

    7

    h-index

  3. MR
    Michael R. Lyu

    Chinese University of Hong Kong, Hong Kong SAR China

    6 publications · 1.9K citations

    6

    h-index

  4. HS
    Hanhuai Shan

    University of Minnesota, United States

    5 publications · 562 citations

    5

    h-index

  5. HM
    Hao Ma

    Chinese University of Hong Kong, Hong Kong SAR China

    4 publications · 1.8K citations

    4

    h-index

1–5 of 796

Venues

  1. Neurocomputing

    11 publications · 419 citations

    11

    h-index

  2. arXiv (Cornell University)

    Repository · 13 publications · 497 citations

    11

    h-index

  3. IEEE Access

    10 publications · 367 citations

    10

    h-index

  4. Knowledge-Based Systems

    9 publications · 690 citations

    9

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 506 citations

    9

    h-index

1–5