Recommender Evaluation Metrics is a systematic framework used to assess the effectiveness, accuracy, and relevance of recommendations generated by recommender systems through quantitative measures such as precision, recall, and normalized discounted cumulative gain.
284
Publications
36K
Citations
712
Authors
308
Institutions
Publications per year
2017–2026
133
Parents
Publications
284
Methods and metrics for cold-start recommendations
Andrew I. Schein, Alexandrin Popescul, Lyle Ungar et al. · 2002 · 1.7K citations · Full text
Performance of recommender algorithms on top-n recommendation tasks
Paolo Cremonesi, Yehuda Koren, Roberto Turrin · 2010 · 1.4K citations
1–5 of 284
Authors
712
- PC
11
h-index
- AB
10
h-index
- AS
9
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- JA
9
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7
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1–5 of 712
Institutions
308
Universidad Autónoma de Madrid
Madrid, Spain
14 h-index
31 publications
2.7K citations
University of Minnesota
Minneapolis, United States
10 h-index
25 publications
22.4K citations
Politecnico di Milano
Milan, Italy
7 h-index
16 publications
2.2K citations
Delft University of Technology
Delft, The Netherlands
7 h-index
9 publications
632 citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
6 h-index
13 publications
3.1K citations
Nanyang Technological University
Singapore, Singapore
6 h-index
11 publications
910 citations
Eindhoven University of Technology
Eindhoven, The Netherlands
6 h-index
11 publications
382 citations
Microsoft (United States)
Redmond, United States
5 h-index
11 publications
18.3K citations
1–8 of 308
Venues
- arXiv (Cornell University)
Repository · 10 publications · 6.3K citations
10
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- Kidney International Supplements
6 publications · 785 citations
6
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- Expert Systems with Applications
6 publications · 523 citations
6
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- Information Sciences
5 publications · 347 citations
5
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- ACM Transactions on Recommender Systems
5 publications · 90 citations
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1–5