Preference Learning For Ranking

Preference Learning for Ranking is a machine learning approach that belongs to the broader class of ranking methods, differing specifically in its focus on inferring a ranking function by modeling relative preferences between pairs or sets of items rather than relying on absolute relevance scores.

55

Publications

3.5K

Citations

156

Authors

92

Institutions

Publications per year

2017–2026

20

Authors

156

  1. EH
    Eyke Hüllermeier

    Philipps University of Marburg, Germany

    3 publications · 113 citations

    3

    h-index

  2. YS
    Yoram Singer

    Hebrew University of Jerusalem, Israel

    3 publications · 1K citations

    3

    h-index

  3. XG
    Xin Geng

    Southeast University, China

    2 publications · 66 citations

    2

    h-index

  4. FH
    Fei Huang

    Alibaba Group (United States), United States

    2 publications · 64 citations

    2

    h-index

  5. AS
    Alessandro Sperduti

    University of Padua, Italy

    2 publications · 41 citations

    2

    h-index

1–5 of 156

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 213 citations

    4

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 62 citations

    2

    h-index

  3. Machine Learning

    2 publications · 270 citations

    2

    h-index

  4. Omega

    2 publications · 65 citations

    1

    h-index

  5. Knowledge-Based Systems

    1 publications · 17 citations

    1

    h-index

1–5