Collaborative Filtering For Search

Collaborative Filtering for Search is a personalized search method that leverages user interaction data to predict relevance by identifying patterns and similarities among users' search behaviors, distinguishing itself from other search personalization techniques through its reliance on collective behavioral signals rather than explicit content analysis or individual profile data.

84

Publications

8.4K

Citations

237

Authors

101

Institutions

Publications per year

2017–2026

20

Authors

237

  1. JA
    Joseph A. Konstan

    University of Minnesota, United States

    4 publications · 5.1K citations

    4

    h-index

  2. JR
    John Riedl

    University of Minnesota, United States

    4 publications · 5.1K citations

    4

    h-index

  3. SD
    Susan Dumais

    Microsoft (United States), United States

    3 publications · 113 citations

    3

    h-index

  4. RF
    Raya Fidel

    University of Washington, United States

    3 publications · 113 citations

    3

    h-index

  5. AM
    Annelise Mark Pejtersen

    University of Washington, United States

    3 publications · 113 citations

    3

    h-index

1–5 of 237

Venues

  1. Information Processing & Management

    2 publications · 79 citations

    2

    h-index

  2. Future Generation Computer Systems

    FGCS · 2 publications · 67 citations

    2

    h-index

  3. Journal of Physics Conference Series

    2 publications · 28 citations

    2

    h-index

  4. 2

    h-index

  5. IEEE Systems Journal

    ISJ · 1 publications · 30 citations

    1

    h-index

1–5