Serendipity Evaluation

Serendipity Evaluation is a methodological framework within recommendation evaluation that assesses the extent to which a recommender system generates unexpected yet relevant and valuable items for users, distinguishing itself from traditional accuracy- or diversity-focused metrics by prioritizing the discovery of non-obvious, surprising recommendations that align with user preferences.

27

Publications

795

Citations

61

Authors

23

Institutions

Publications per year

2017–2026

13

Authors

61

  1. DK
    Denis Kotkov

    University of Jyväskylä, Finland

    3 publications · 112 citations

    3

    h-index

  2. XN
    Xi Niu

    University of North Carolina at Charlotte, United States

    3 publications · 47 citations

    3

    h-index

  3. DY
    Diyi Yang

    Georgia Institute of Technology, United States

    2 publications · 80 citations

    2

    h-index

  4. SM
    Stephann Makri

    City, University of London, United Kingdom

    2 publications · 30 citations

    2

    h-index

  5. SW
    Shuaiqiang Wang

    Baidu (China), China

    2 publications · 87 citations

    2

    h-index

1–5 of 61

Venues

  1. Electronic workshops in computing

    1 publications · 13 citations

    1

    h-index

  2. Journal of Computer Science and Technology

    1 publications · 116 citations

    1

    h-index

  3. 1

    h-index

  4. Procedia Computer Science

    1 publications · 22 citations

    1

    h-index

  5. The Journal of Creative Behavior

    JCB · 1 publications · 30 citations

    1

    h-index

1–5