Pairwise Ranking Optimization

Pairwise Ranking Optimization is a machine learning methodology that, within the context of implicit feedback modeling, optimizes the relative ordering of user-item interactions by minimizing a loss function defined over pairs of observed and unobserved preferences, thereby distinguishing itself from pointwise approaches through its focus on comparative, rather than absolute, relevance predictions.

30

Publications

2K

Citations

116

Authors

50

Institutions

Publications per year

2017–2026

19

Authors

116

  1. XH
    Xiangnan He

    University of Science and Technology of China, China

    4 publications · 561 citations

    4

    h-index

  2. JD
    Jingtao Ding

    Tsinghua University, China

    3 publications · 160 citations

    3

    h-index

  3. DJ
    Depeng Jin

    Tsinghua University, China

    3 publications · 160 citations

    3

    h-index

  4. YL
    Yong Li

    Tsinghua University, China

    3 publications · 160 citations

    3

    h-index

  5. XW
    Xuanhui Wang

    Google (United States), United States

    3 publications · 162 citations

    3

    h-index

1–5 of 116

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 29 citations

    2

    h-index

  2. Journal of the Operational Research Society

    OR · 1 publications · 11 citations

    1

    h-index

  3. Cluster Computing

    1 publications · 10 citations

    1

    h-index

  4. ACM Transactions on Intelligent Systems and Technology

    TIST · 1 publications · 11 citations

    1

    h-index

  5. The Annals of Mathematical Statistics

    1 publications · 34 citations

    1

    h-index

1–5