User Preference Inference

User Preference Inference is a computational methodology within implicit feedback modeling that derives latent user preferences from observable interaction behaviors by distinguishing underlying preference signals from noise and confounding factors inherent in implicit data.

147

Publications

9.7K

Citations

566

Authors

202

Institutions

Publications per year

2017–2026

87

Authors

566

  1. LN
    Liqiang Nie

    Shandong University, China

    5 publications · 1.4K citations

    5

    h-index

  2. DM
    David M. Blei

    Princeton University, United States

    3 publications · 351 citations

    3

    h-index

  3. RH
    Richang Hong

    Hefei University of Technology, China

    3 publications · 168 citations

    3

    h-index

  4. ZS
    Zhu Sun

    Nanyang Technological University, Singapore

    3 publications · 82 citations

    3

    h-index

  5. XH
    Xiangnan He

    University of Science and Technology of China, China

    3 publications · 1.3K citations

    3

    h-index

1–5 of 566

Venues

  1. 5

    h-index

  2. ACM Transactions on Information Systems

    5 publications · 116 citations

    5

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 175 citations

    5

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 242 citations

    5

    h-index

  5. Expert Systems with Applications

    4 publications · 83 citations

    4

    h-index

1–5