User-based Collaborative Filtering

User-Based Collaborative Filtering is a collaborative filtering method that predicts a user's preferences by aggregating the opinions of users with similar past behavior, distinguished from item-based or model-based approaches by its reliance on user-user similarity computed from rating patterns.

571

Publications

43.5K

Citations

1.5K

Authors

583

Institutions

Publications per year

2017–2026

209

Variants

User-based Cf

Authors

1.5K

  1. JA
    Joseph A. Konstan

    University of Minnesota, United States

    6 publications · 10.9K citations

    6

    h-index

  2. LS
    Luo Si

    Purdue University West Lafayette, United States

    6 publications · 832 citations

    6

    h-index

  3. DM
    Dionisis Margaris

    University of Peloponnese, Greece

    5 publications · 129 citations

    5

    h-index

  4. CV
    Costas Vassilakis

    University of Peloponnese, Greece

    5 publications · 129 citations

    5

    h-index

  5. AB
    Alejandro Bellogín

    Universidad Autónoma de Madrid, Spain

    4 publications · 193 citations

    4

    h-index

1–5 of 1.5K

Venues

  1. Expert Systems with Applications

    20 publications · 1.6K citations

    19

    h-index

  2. Procedia Computer Science

    9 publications · 324 citations

    9

    h-index

  3. Applied Intelligence

    8 publications · 324 citations

    8

    h-index

  4. Journal of Physics Conference Series

    6 publications · 130 citations

    6

    h-index

  5. Neurocomputing

    6 publications · 259 citations

    6

    h-index

1–5