Probabilistic Relevance Estimation

Probabilistic Relevance Estimation is a formal framework in information retrieval that models the likelihood of a document being relevant to a query by computing probabilities based on evidence from document-query relationships, distinguishing itself from deterministic or heuristic approaches through its foundation in probabilistic inference.

51

Publications

6.5K

Citations

122

Authors

55

Institutions

Publications per year

2017–2026

6

Authors

122

  1. WB
    W. Bruce Croft

    University of Massachusetts Amherst, United States

    5 publications · 2.9K citations

    5

    h-index

  2. JG
    Jiafeng Guo

    Chinese Academy of Sciences, China

    3 publications · 358 citations

    3

    h-index

  3. AP
    Arjen P. de Vries

    Centrum Wiskunde & Informatica, Netherlands

    3 publications · 122 citations

    3

    h-index

  4. NF
    Norbert Fuhr

    University of Duisburg-Essen, Germany

    3 publications · 255 citations

    3

    h-index

  5. XC
    Xueqi Cheng

    Chinese Academy of Sciences, China

    3 publications · 358 citations

    3

    h-index

1–5 of 122

Venues

  1. Journal of the American Society for Information Science

    JASIS · 2 publications · 87 citations

    2

    h-index

  2. Text REtrieval Conference

    Conference · 2 publications · 98 citations

    2

    h-index

  3. ACM Transactions on Information Systems

    2 publications · 224 citations

    2

    h-index

  4. University of Twente Research Information

    Repository · 1 publications · 12 citations

    1

    h-index

  5. Journal of Intelligent Manufacturing

    1 publications · 28 citations

    1

    h-index

1–5