Probabilistic Preference Modeling

Probabilistic Preference Modeling is a preference modeling framework that characterizes uncertain or stochastic user preferences through probability distributions over preference relations, distinguishing itself from deterministic models by explicitly accounting for variability, inconsistency, or confidence in individual choices.

120

Publications

5.8K

Citations

277

Authors

162

Institutions

Publications per year

2017–2026

38

Authors

277

  1. ZX
    Zeshui Xu

    Sichuan University, China

    5 publications · 335 citations

    5

    h-index

  2. HL
    Huchang Liao

    Sichuan University, China

    4 publications · 471 citations

    4

    h-index

  3. MR
    Michel Regenwetter

    University of Illinois Urbana-Champaign, United States

    4 publications · 196 citations

    4

    h-index

  4. AA
    A. A. J. Marley

    University of Victoria, Canada

    3 publications · 255 citations

    3

    h-index

  5. JZ
    Jianming Zhan

    Minzu University of China, China

    3 publications · 64 citations

    3

    h-index

1–5 of 277

Venues

  1. Information Sciences

    6 publications · 304 citations

    6

    h-index

  2. Mathematical Social Sciences

    5 publications · 105 citations

    5

    h-index

  3. Social Choice and Welfare

    4 publications · 102 citations

    4

    h-index

  4. Journal of Mathematical Psychology

    4 publications · 183 citations

    4

    h-index

  5. arXiv (Cornell University)

    Repository · 4 publications · 118 citations

    4

    h-index

1–5