Interactive Preference Learning
Interactive Preference Learning is a machine learning framework that iteratively elicits and models user preferences through active interaction, distinguishing itself by integrating real-time feedback into the optimization process to align decision outcomes with subjective human values in complex, multi-objective environments.
21
Publications
816
Citations
66
Authors
43
Institutions
Publications per year
2017–2026
9
21
Robust ordinal regression in preference learning and ranking
Salvatore Corrente, Salvatore Greco, Miłosz Kadziński et al. · Machine Learning · 2013 · 211 citations · Full text
Intelligent profiling by example
Sybil Shearin, Henry Lieberman · 2001 · 123 citations
Preference elicitation for interface optimization
Krzysztof Z. Gajos, Daniel S. Weld · 2005 · 106 citations · Full text
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43

Centre National de la Recherche Scientifique
Paris, France
3 h-index
8 publications
142 citations
University of Michigan
Ann Arbor, United States
3 h-index
7 publications
273 citations

Technische Universität Darmstadt
Darmstadt, Germany
2 h-index
6 publications
147 citations

University of Amsterdam
Amsterdam, The Netherlands
1 h-index
2 publications
24 citations
Amsterdam University of the Arts
Amsterdam, The Netherlands
1 h-index
2 publications
24 citations
University of Florence
Florence, Italy
1 h-index
3 publications
42 citations
Menlo School
Atherton, United States
1 h-index
2 publications
114 citations

Université Toulouse-I-Capitole
Toulouse, France
1 h-index
3 publications
30 citations
1–8 of 43
1 publications · 211 citations
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IJAIT · 1 publications · 26 citations
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Conference · 1 publications · 25 citations
1
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ITDM · 1 publications · 18 citations
1
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Conference · 1 publications · 10 citations
1
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