Collaborative Filtering

Collaborative Filtering is a recommendation technique that predicts a user's preferences by leveraging the collective behavior of other users, distinguishing itself through its reliance on user-item interaction patterns rather than content-based features.

8.3K

Publications

633.1K

Citations

17.5K

Authors

3.1K

Institutions

Publications per year

2017–2026

4.2K

Authors

17.5K

  1. XH
    Xiangnan He

    University of Science and Technology of China, China

    68 publications · 20.7K citations

    42

    h-index

  2. JA
    Joseph A. Konstan

    University of Minnesota, United States

    45 publications · 39.9K citations

    38

    h-index

  3. JR
    John Riedl

    University of Minnesota, United States

    41 publications · 39.1K citations

    34

    h-index

  4. BM
    Bamshad Mobasher

    DePaul University, United States

    44 publications · 5.1K citations

    31

    h-index

  5. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    37 publications · 11.9K citations

    30

    h-index

1–5 of 17.5K

Venues

  1. Expert Systems with Applications

    336 publications · 22.3K citations

    80

    h-index

  2. Knowledge-Based Systems

    208 publications · 14.3K citations

    64

    h-index

  3. Information Sciences

    163 publications · 10K citations

    53

    h-index

  4. arXiv (Cornell University)

    Repository · 220 publications · 16.9K citations

    53

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 90 publications · 19.6K citations

    46

    h-index

1–5