Annotation Recommendation

Annotation Recommendation is a computational method that selectively identifies and proposes contextual additions to visualizations by analyzing content relevance, user needs, and perceptual effectiveness, thereby enhancing information comprehension and analytical insight.

20

Publications

1.5K

Citations

66

Authors

33

Institutions

Publications per year

2017–2026

9

Authors

66

  1. BS
    Bageshree Shevade

    Arizona State University, United States

    3 publications · 63 citations

    3

    h-index

  2. HS
    Hari Sundaram

    Arizona State University, United States

    3 publications · 63 citations

    3

    h-index

  3. LX
    Lexing Xie

    Australian National University, Australia

    2 publications · 53 citations

    2

    h-index

  4. TS
    Thomas Schimoler

    DePaul University, United States

    2 publications · 65 citations

    2

    h-index

  5. RB
    Robin Burke

    DePaul University, United States

    2 publications · 65 citations

    2

    h-index

1–5 of 66

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 49 citations

    2

    h-index

  2. Journal of Computer and System Sciences

    1 publications · 51 citations

    1

    h-index

  3. Procedia Computer Science

    1 publications · 19 citations

    1

    h-index

  4. Surgical Endoscopy

    1 publications · 108 citations

    1

    h-index

  5. International Journal of Artificial Intelligence Tools

    IJAIT · 1 publications · 11 citations

    1

    h-index

1–5