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

Authors

71

  1. AB
    Alexander Benlian

    Technische Universität Darmstadt, Germany

    3 publications · 178 citations

    3

    h-index

  2. CM
    Christian Matt

    University of Bern, Switzerland

    2 publications · 37 citations

    2

    h-index

  3. TM
    Thomas M. Hess

    North Carolina State University, United States

    2 publications · 37 citations

    2

    h-index

  4. MZ
    Michele Zanitti

    Aalborg University, Denmark

    1 publications · 15 citations

    1

    h-index

  5. LP
    Lorenzo Porcaro

    Universitat Pompeu Fabra, Spain

    1 publications · 25 citations

    1

    h-index

1–5 of 71

Venues

  1. Journal of Management Information Systems

    JMIS · 2 publications · 174 citations

    2

    h-index

  2. Management Science

    2 publications · 83 citations

    2

    h-index

  3. Decision Support Systems

    1 publications · 193 citations

    1

    h-index

  4. Open Access CRIS of the University of Bern

    Repository · 1 publications · 17 citations

    1

    h-index

  5. 1

    h-index

1–5