Implicit Feedback Modeling

Implicit Feedback Modeling is a recommendation modeling approach that learns user preferences from indirect behavioral signals such as clicks, views, and purchases—rather than from explicit ratings or stated preferences—by treating these observed interactions as noisy, one-sided indications of relevance.

275

Publications

47.4K

Citations

830

Authors

254

Institutions

Publications per year

2017–2026

161

Authors

830

  1. XH
    Xiangnan He

    University of Science and Technology of China, China

    10 publications · 8.8K citations

    10

    h-index

  2. JM
    Joemon M. Jose

    University of Glasgow, United Kingdom

    11 publications · 586 citations

    10

    h-index

  3. TJ
    Thorsten Joachims

    Cornell University, United States

    10 publications · 857 citations

    10

    h-index

  4. DL
    Defu Lian

    University of Science and Technology of China, China

    8 publications · 868 citations

    8

    h-index

  5. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    7 publications · 8.7K citations

    7

    h-index

1–5 of 830

Venues

  1. arXiv (Cornell University)

    Repository · 20 publications · 5.3K citations

    13

    h-index

  2. IEEE Access

    7 publications · 132 citations

    7

    h-index

  3. ACM Transactions on Information Systems

    7 publications · 438 citations

    7

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 1.2K citations

    7

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 252 citations

    7

    h-index

1–5