Iterative Relevance Feedback

Iterative Relevance Feedback is a query refinement method in information retrieval that systematically incorporates user feedback across multiple cycles to progressively improve retrieval accuracy by adjusting the query representation based on relevance judgments of previously retrieved documents.

31

Publications

4K

Citations

111

Authors

55

Institutions

Publications per year

2017–2026

4

Authors

111

  1. JR
    Jason R. Baron

    University of Maryland, College Park, United States

    3 publications · 172 citations

    3

    h-index

  2. DW
    Douglas W. Oard

    University of Maryland, College Park, United States

    3 publications · 172 citations

    3

    h-index

  3. ST
    Stephen Tomlinson

    Medical University of South Carolina, United States

    3 publications · 172 citations

    3

    h-index

  4. MD
    Mark D. Smucker

    University of Waterloo, Canada

    2 publications · 88 citations

    2

    h-index

  5. BB
    Bir Bhanu

    University of California, Riverside, United States

    2 publications · 58 citations

    2

    h-index

1–5 of 111

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 206 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 1 publications · 11 citations

    1

    h-index

  3. 1

    h-index

  4. Journal of the American Society for Information Science

    JASIS · 1 publications · 1.5K citations

    1

    h-index

  5. JCO Precision Oncology

    1 publications · 48 citations

    1

    h-index

1–5