Probabilistic Relevance Feedback

Probabilistic Relevance Feedback is a relevance feedback method that relies on probabilistic models to estimate the likelihood of document relevance based on user feedback, thereby refining information retrieval through statistical inference.

65

Publications

4.9K

Citations

133

Authors

76

Institutions

Publications per year

2017–2026

4

Authors

133

  1. PY
    P.N. Yianilos

    Princeton University, United States

    4 publications · 1.1K citations

    4

    h-index

  2. IJ
    Ingemar J. Cox

    University College London, United Kingdom

    4 publications · 1.1K citations

    4

    h-index

  3. ML
    Matthew L. Miller

    Princeton University, United States

    4 publications · 1.1K citations

    4

    h-index

  4. SR
    Stephen Robertson

    Microsoft Research (United Kingdom), United Kingdom

    3 publications · 184 citations

    3

    h-index

  5. DZ
    Djemel Ziou

    Université de Sherbrooke, Canada

    3 publications · 120 citations

    3

    h-index

1–5 of 133

Venues

  1. ACM Transactions on Information Systems

    3 publications · 278 citations

    3

    h-index

  2. IEEE Transactions on Image Processing

    3 publications · 838 citations

    3

    h-index

  3. Pattern Recognition

    3 publications · 144 citations

    3

    h-index

  4. Information Processing & Management

    2 publications · 67 citations

    2

    h-index

  5. 2

    h-index

1–5