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
Parents
27
Challenges of Serendipity in Recommender Systems
Denis Kotkov, Jari Veijalainen, Shuaiqiang Wang · 2016 · 75 citations · Full text
Serendipitous Personalized Ranking for Top-N Recommendation
Qiuxia Lu, Tianqi Chen, Weinan Zhang et al. · 2012 · 44 citations
Valentina Maccatrozzo, Manon Terstall, Lora Aroyo et al. · 2017 · 39 citations · Full text
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23

University of North Carolina at Charlotte
Charlotte, United States
3 h-index
6 publications
94 citations
Islamic Azad University Central Tehran Branch
Tehran, Iran
2 h-index
4 publications
294 citations

University of Jyväskylä
Jyväskylä, Finland
2 h-index
5 publications
249 citations

University of Dundee
Dundee, United Kingdom
2 h-index
4 publications
74 citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
10 publications
400 citations
Technion – Israel Institute of Technology
Haifa, Israel
1 h-index
1 publications
15 citations

University of Helsinki
Helsinki, Finland
1 h-index
3 publications
75 citations

Renmin University of China
Beijing, China
1 h-index
1 publications
16 citations
1–8 of 23
1 publications · 13 citations
1
h-index
1 publications · 116 citations
1
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Repository · 1 publications · 50 citations
1
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1 publications · 22 citations
1
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JCB · 1 publications · 30 citations
1
h-index
1–5