Vector Space Retrieval

Vector Space Retrieval is an information retrieval model that represents documents and queries as vectors in a high-dimensional space, where similarity is determined by the cosine of the angle between vectors, distinguishing it from other retrieval models by its use of geometric relationships to rank document relevance.

365

Publications

37.3K

Citations

924

Authors

372

Institutions

Publications per year

2017–2026

59

Authors

924

  1. AM
    Alistair Moffat

    The University of Melbourne, Australia

    7 publications · 417 citations

    7

    h-index

  2. SK
    S. K. M. Wong

    University of Regina, Canada

    7 publications · 881 citations

    7

    h-index

  3. OF
    Ophir Frieder

    Illinois Institute of Technology, United States

    6 publications · 145 citations

    6

    h-index

  4. HJ
    Hervé Jeǵou

    Institut national de recherche en sciences et technologies du numérique, France

    5 publications · 942 citations

    5

    h-index

  5. VA
    Vassilis Athitsos

    The University of Texas at Arlington, United States

    5 publications · 223 citations

    5

    h-index

1–5 of 924

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 667 citations

    11

    h-index

  2. ACM Transactions on Information Systems

    9 publications · 500 citations

    9

    h-index

  3. Text REtrieval Conference

    Conference · 8 publications · 1.2K citations

    8

    h-index

  4. Information Processing & Management

    8 publications · 499 citations

    8

    h-index

  5. Journal of the American Society for Information Science

    JASIS · 8 publications · 467 citations

    8

    h-index

1–5