Ranking Metrics
Ranking Metrics is a computational evaluation framework used in recommender systems that quantifies the effectiveness of ranked item lists by measuring the position and relevance of recommended items relative to user preferences.
505
Publications
57K
Citations
1.4K
Authors
529
Institutions
Publications per year
2017–2026
204
Variants
Ranking Evaluation Metrics
505
The PageRank Citation Ranking : Bringing Order to the Web
Lawrence M. Page, Sergey Brin, Rajeev Motwani et al. · 1999 · 12.7K citations
Analysis of recommendation algorithms for e-commerce
Badrul Sarwar, George Karypis, Joseph A. Konstan et al. · 2000 · 1.8K citations · Full text
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
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529

Universidad Autónoma de Madrid
Madrid, Spain
13 h-index
25 publications
2.2K citations

University of Minnesota
Minneapolis, United States
13 h-index
35 publications
30.3K citations
Google (United States)
Mountain View, United States
12 h-index
38 publications
16K citations

Delft University of Technology
Delft, The Netherlands
11 h-index
21 publications
1.7K citations
Microsoft (United States)
Redmond, United States
11 h-index
18 publications
741 citations
Nanyang Technological University
Singapore, Singapore
9 h-index
21 publications
850 citations

Beihang University
Beijing, China
9 h-index
29 publications
772 citations
Tsinghua University
Beijing, China
8 h-index
20 publications
1.7K citations
1–8 of 529
13 publications · 679 citations
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Repository · 13 publications · 400 citations
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10 publications · 794 citations
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9 publications · 661 citations
9
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JBR · 5 publications · 778 citations
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