Machine Learning For Personalization

Machine Learning for Personalization is a computational methodology that employs machine learning algorithms to dynamically model and adapt user-specific preferences, behaviors, and needs within digital environments, distinguishing it from general machine learning approaches by its focus on individualized prediction, recommendation, and interface customization in contexts such as personalized learning environments and web personalization.

256

Publications

11.6K

Citations

844

Authors

368

Institutions

Publications per year

2017–2026

163

Authors

844

  1. NL
    Nissan Levin

    Tel Aviv University, Israel

    3 publications · 170 citations

    3

    h-index

  2. JZ
    Jacob Zahavi

    Tel Aviv University, Israel

    3 publications · 170 citations

    3

    h-index

  3. BS
    Barry Smyth

    University College Dublin, Ireland

    3 publications · 204 citations

    3

    h-index

  4. VL
    Vasyl Lytvyn

    Lviv Polytechnic National University, Ukraine

    2 publications · 52 citations

    2

    h-index

  5. XC
    Xiaolong Chen

    Institute of Atmospheric Physics, China

    2 publications · 309 citations

    2

    h-index

1–5 of 844

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 400 citations

    6

    h-index

  2. Expert Systems with Applications

    6 publications · 676 citations

    6

    h-index

  3. IEEE Access

    6 publications · 368 citations

    6

    h-index

  4. Journal of Physics Conference Series

    4 publications · 90 citations

    4

    h-index

  5. Procedia Computer Science

    4 publications · 139 citations

    4

    h-index

1–5