Bayesian Collaborative Filtering

Bayesian Collaborative Filtering is a probabilistic collaborative filtering method that employs Bayesian inference to model user-item interactions by incorporating prior distributions over latent factors and updating them based on observed preferences, thereby enabling uncertainty-aware predictions and personalized recommendations.

84

Publications

8.2K

Citations

265

Authors

128

Institutions

Publications per year

2017–2026

33

Authors

265

  1. GG
    Guibing Guo

    Northeastern University, China

    3 publications · 315 citations

    3

    h-index

  2. HZ
    Hengshu Zhu

    Baidu (China), China

    3 publications · 96 citations

    3

    h-index

  3. JZ
    Jie Zhang

    Nanyang Technological University, Singapore

    3 publications · 318 citations

    3

    h-index

  4. CZ
    Chen Zhu

    University of Science and Technology of China, China

    2 publications · 57 citations

    2

    h-index

  5. JF
    Juan F. Huete

    Universidad de Granada, Spain

    2 publications · 337 citations

    2

    h-index

1–5 of 265

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 127 citations

    4

    h-index

  2. Neurocomputing

    2 publications · 51 citations

    2

    h-index

  3. Knowledge-Based Systems

    2 publications · 324 citations

    2

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 114 citations

    2

    h-index

  5. IEEE Access

    2 publications · 120 citations

    2

    h-index

1–5