Interactive Relevance Feedback

Interactive Relevance Feedback is a relevance feedback paradigm in which users iteratively refine information retrieval results through explicit, real-time interactions that directly inform subsequent query modifications or system adaptations.

101

Publications

8.6K

Citations

279

Authors

113

Institutions

Publications per year

2017–2026

12

Authors

279

  1. AL
    Anton Leuski

    University of Southern California, United States

    4 publications · 307 citations

    4

    h-index

  2. BÞ
    Björn Þór Jónsson

    Reykjavík University, Iceland

    3 publications · 55 citations

    3

    h-index

  3. JR
    Jason R. Baron

    University of Maryland, College Park, United States

    3 publications · 172 citations

    3

    h-index

  4. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    3 publications · 70 citations

    3

    h-index

  5. ST
    Stephen Tomlinson

    Medical University of South Carolina, United States

    3 publications · 172 citations

    3

    h-index

1–5 of 279

Venues

  1. Journal of Documentation

    3 publications · 85 citations

    3

    h-index

  2. 3

    h-index

  3. Journal of the American Society for Information Science

    JASIS · 2 publications · 143 citations

    2

    h-index

  4. 2

    h-index

  5. Information Processing & Management

    2 publications · 260 citations

    2

    h-index

1–5