Active Learning For Filtering
Active Learning for Filtering is an information filtering methodology that selectively queries the most informative instances for labeling in order to optimize model performance with minimal human annotation effort.
40
Publications
2.3K
Citations
115
Authors
41
Institutions
Publications per year
2017–2026
12
Parents
40
Personalized active learning for collaborative filtering
Abhay Harpale, Yiming Yang · 2008 · 122 citations
1–5 of 40
115
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2
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National Centre of Scientific Research "Demokritos", Greece
2 publications · 33 citations
2
h-index
1–5 of 115
41
Carnegie Mellon University
Pittsburgh, United States
6 h-index
14 publications
1.3K citations
Tufts University
Medford, United States
4 h-index
5 publications
146 citations
Utrecht University
Utrecht, The Netherlands
2 h-index
8 publications
672 citations
University of Stuttgart
Stuttgart, Germany
2 h-index
7 publications
840 citations
National Centre of Scientific Research "Demokritos"
Athens, Greece
2 h-index
5 publications
79 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
2 h-index
4 publications
140 citations
The University of Texas at Austin
Austin, United States
1 h-index
2 publications
170 citations

Osnabrück University
Osnabrück, Germany
1 h-index
2 publications
176 citations
1–8 of 41
1 publications · 84 citations
2
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Conference · 1 publications · 20 citations
1
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TOSEM · 1 publications · 33 citations
1
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1 publications · 54 citations
1
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1 publications · 300 citations
1
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1–5