Unexpected Recommendation
Unexpected Recommendation is a recommendation approach that belongs to the class of novelty-oriented methods in information retrieval, distinguished by its deliberate generation of recommendations that diverge significantly from users' established preferences or predictable patterns, thereby introducing serendipitous or surprising items not readily anticipated by the user or standard predictive models.
20
Publications
906
Citations
49
Authors
24
Institutions
Publications per year
2017–2026
13
Parents
20
Investigating serendipity in recommender systems based on real user feedback
Denis Kotkov, Joseph A. Konstan, Qian Zhao et al. · 2018 · 97 citations
Serendipitous recommendations via innovators
Noriaki Kawamae · 2010 · 62 citations
1–5 of 20
49
5
h-index
4
h-index
3
h-index
2
h-index
2
h-index
1–5 of 49
24
New York University
New York, United States
5 h-index
8 publications
729 citations

University of North Carolina at Charlotte
Charlotte, United States
2 h-index
3 publications
56 citations
Emory University
Atlanta, United States
2 h-index
2 publications
135 citations

University of Jyväskylä
Jyväskylä, Finland
2 h-index
4 publications
218 citations

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

Cyber University
Fukuoka, Japan
1 h-index
1 publications
58 citations
Kobe International University
Kobe, Japan
1 h-index
1 publications
58 citations
Toyota Motor Corporation (Japan)
Toyota, Japan
1 h-index
2 publications
28 citations
1–8 of 24
TIST · 2 publications · 244 citations
2
h-index
IJASEIT · 1 publications · 16 citations
1
h-index
Repository · 1 publications · 64 citations
1
h-index
1 publications · 39 citations
1
h-index
1 publications · 19 citations
1
h-index
1–5