Implicit Collaborative Filtering

Implicit Collaborative Filtering is a collaborative filtering approach that infers user preferences through implicitly observed behaviors—such as clicks, views, or purchases—rather than explicit ratings, by modeling patterns of user-item interactions under the assumption that observed engagement reflects underlying preference.

21

Publications

2.3K

Citations

77

Authors

42

Institutions

Publications per year

2017–2026

12

Variants

Implicit Feedback Collaborative Filtering

Authors

77

  1. XX
    Xi Xiong

    Chengdu University of Information Technology, China

    1 publications · 47 citations

    1

    h-index

  2. YH
    Yutian Hu

    Beijing Jiaotong University, China

    1 publications · 47 citations

    1

    h-index

  3. XH
    Xiangnan He

    University of Science and Technology of China, China

    1 publications · 876 citations

    1

    h-index

  4. KC
    Ke Chen

    University of Liverpool, United Kingdom

    1 publications · 33 citations

    1

    h-index

  5. LZ
    Lianfen Zhao

    Institute of Software, China

    1 publications · 16 citations

    1

    h-index

1–5 of 77

Venues

  1. Neurocomputing

    2 publications · 83 citations

    2

    h-index

  2. Information Sciences

    2 publications · 241 citations

    2

    h-index

  3. 1

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 22 citations

    1

    h-index

  5. Data & Knowledge Engineering

    DKE · 1 publications · 57 citations

    1

    h-index

1–5