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
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3.5K
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156
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92
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2017–2026
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An Efficient Boosting Algorithm for Combining Preferences
Yoav Freund, Raj D. Iyer, Robert E. Schapire et al. · 1998 · 815 citations
Journal of Artificial Intelligence Research · 1999 · 313 citations · Full text
Robust ordinal regression in preference learning and ranking
Salvatore Corrente, Salvatore Greco, Miłosz Kadziński et al. · Machine Learning · 2013 · 211 citations · Full text
Log-Linear Models for Label Ranking
Ofer Dekel, Yoram Singer, Christopher D. Manning · 2003 · 166 citations
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Poznań University of Technology
Poznan, Poland
3 h-index
5 publications
615 citations
Microsoft Research Asia (China)
Beijing, China
3 h-index
6 publications
382 citations
California Institute of Technology
Pasadena, United States
2 h-index
2 publications
43 citations
Georgia Institute of Technology
Atlanta, United States
2 h-index
3 publications
62 citations

Paderborn University
Paderborn, Germany
2 h-index
3 publications
48 citations

Southeast University
Nanjing, China
2 h-index
6 publications
160 citations

University of Turku
Turku, Finland
2 h-index
8 publications
325 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
2 publications
154 citations
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Repository · 4 publications · 213 citations
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Conference · 2 publications · 62 citations
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2 publications · 270 citations
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2 publications · 65 citations
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1 publications · 17 citations
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