Rating Matrix Completion

Rating Matrix Completion is a predictive modeling task in recommendation systems that involves estimating missing entries in a user-item rating matrix by leveraging observed ratings and structural patterns, distinguishing itself from general prediction tasks through its focus on recovering latent user preferences and item characteristics within sparse observational data.

38

Publications

3.3K

Citations

114

Authors

56

Institutions

Publications per year

2017–2026

16

Authors

114

  1. IB
    Iman Barjasteh

    Michigan State University, United States

    2 publications · 140 citations

    2

    h-index

  2. RF
    Rana Forsati

    Shahid Beheshti University, Iran

    2 publications · 140 citations

    2

    h-index

  3. HR
    Hayder Radha

    Michigan State University, United States

    2 publications · 140 citations

    2

    h-index

  4. AE
    Abdol‐Hossein Esfahanian

    Michigan State University, United States

    2 publications · 140 citations

    2

    h-index

  5. JT
    James T. Kwok

    Hong Kong University of Science and Technology, Hong Kong SAR China

    1 publications · 242 citations

    1

    h-index

1–5 of 114

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 1.1K citations

    3

    h-index

  2. IEEE/CAA Journal of Automatica Sinica

    1 publications · 28 citations

    1

    h-index

  3. 1

    h-index

  4. Statistical Science

    1 publications · 57 citations

    1

    h-index

  5. Neurocomputing

    1 publications · 21 citations

    1

    h-index

1–5