Binary Independence Model

The Binary Independence Model is a probabilistic retrieval model that assumes binary term occurrence in documents and independence between terms, using these assumptions to estimate the probability of relevance based on the presence or absence of terms in document representations.

14

Publications

426

Citations

26

Authors

14

Institutions

Publications per year

2017–2026

0

Authors

26

  1. TR
    Thomas Roelleke

    Queen Mary University of London, United Kingdom

    3 publications · 176 citations

    3

    h-index

  2. JW
    Jun Wang

    University College London, United Kingdom

    2 publications · 147 citations

    2

    h-index

  3. RM
    Robert M. Losee

    University of North Carolina at Chapel Hill, United States

    2 publications · 25 citations

    2

    h-index

  4. NF
    Norbert Fuhr

    University of Duisburg-Essen, Germany

    2 publications · 51 citations

    2

    h-index

  5. YY
    Yiyu Yao

    University of Regina, Canada

    1 publications · 17 citations

    1

    h-index

1–5 of 26

Venues

  1. Journal of the American Society for Information Science

    JASIS · 2 publications · 27 citations

    2

    h-index

  2. University of Twente Research Information

    Repository · 1 publications · 13 citations

    1

    h-index

  3. ACM Transactions on Database Systems

    TODS · 1 publications · 22 citations

    1

    h-index

  4. ACM Transactions on Information Systems

    1 publications · 25 citations

    1

    h-index

1–4