Personalization Trade-off
Personalization Trade-Off is a theoretical construct in the field of diversity in recommendation that characterizes the inherent tension between optimizing recommendation accuracy for individual user preferences and promoting diverse, serendipitous, or novel content to mitigate filter bubble effects.
21
Publications
870
Citations
71
Authors
41
Institutions
Publications per year
2017–2026
15
Parents
21
Controlling Polarization in Personalization
L. Elisa Celis, Sayash Kapoor, Farnood Salehi et al. · 2019 · 69 citations · Full text
Business, Economics, Public Opinion +16
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41

University of Göttingen
Göttingen, Germany
2 h-index
3 publications
83 citations
Ludwig-Maximilians-Universität München
Munich, Germany
2 h-index
2 publications
46 citations

Technische Universität Darmstadt
Darmstadt, Germany
2 h-index
2 publications
158 citations

University of Minnesota
Minneapolis, United States
2 h-index
2 publications
87 citations
Boston University
Boston, United States
1 h-index
1 publications
54 citations
Copenhagen Business School
Copenhagen, Denmark
1 h-index
1 publications
29 citations
Max Planck Institute for Software Systems
Saarbrücken, Germany
1 h-index
2 publications
44 citations
University of Massachusetts Boston
Boston, United States
1 h-index
1 publications
18 citations
1–8 of 41
JMIS · 2 publications · 174 citations
2
h-index
2 publications · 83 citations
2
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1 publications · 193 citations
1
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Repository · 1 publications · 17 citations
1
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1 publications · 25 citations
1
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1–5