Dashboard Recommender Systems

Dashboard Recommender System is a computational framework that autonomously suggests dashboard configurations or visualizations by analyzing user intent, data characteristics, and design best practices within the context of interactive data visualization.

6

Publications

176

Citations

24

Authors

11

Institutions

Publications per year

2017–2026

6

Authors

24

  1. AW
    Aoyu Wu

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

    2 publications · 78 citations

    2

    h-index

  2. HQ
    Huamin Qu

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

    2 publications · 78 citations

    2

    h-index

  3. XH
    Xinyi He

    Xi'an University of Architecture and Technology, China

    1 publications · 53 citations

    1

    h-index

  4. MZ
    Mengyu Zhou

    Microsoft Research Asia (China), China

    1 publications · 53 citations

    1

    h-index

  5. YW
    Yun Wang

    Microsoft Research Asia (China), China

    1 publications · 53 citations

    1

    h-index

1–5 of 24

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 3 publications · 106 citations

    3

    h-index

  2. Applied Sciences

    1 publications · 25 citations

    1

    h-index

  3. Visual Informatics

    1 publications · 20 citations

    1

    h-index

1–3