Machine Learning-based Personalization

Machine Learning-Based Personalization is a computational approach to personalization in design that employs machine learning algorithms to dynamically adapt content, interfaces, or experiences to individual users based on their behavioral, contextual, or preference data.

83

Publications

3K

Citations

298

Authors

102

Institutions

Publications per year

2017–2026

60

Authors

298

  1. EC
    Enhong Chen

    University of Science and Technology of China, China

    2 publications · 309 citations

    2

    h-index

  2. QL
    Qi Liu

    Peking University, China

    2 publications · 309 citations

    2

    h-index

  3. QY
    Qian Yang

    Carnegie Mellon University, United States

    2 publications · 68 citations

    2

    h-index

  4. NR
    Nataniel Ruiz

    Google (United States), United States

    2 publications · 120 citations

    2

    h-index

  5. YP
    Yael Pritch

    Hebrew University of Jerusalem, Israel

    2 publications · 120 citations

    2

    h-index

1–5 of 298

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 94 citations

    5

    h-index

  2. IEEE Access

    3 publications · 77 citations

    3

    h-index

  3. National Conference on Artificial Intelligence

    Conference · 2 publications · 68 citations

    2

    h-index

  4. Future Internet

    2 publications · 40 citations

    2

    h-index

  5. 2

    h-index

1–5