Ranking Optimization
Ranking Optimization is a recommendation technique that specifically refines the ordering of items in a user's suggested list by jointly leveraging multiple criteria and dynamic user feedback to maximize relevance and personalization in real-time collaborative filtering systems.
240
Publications
13.9K
Citations
733
Authors
314
Institutions
Publications per year
2017–2026
99
240
Analysis of recommendation algorithms for e-commerce
Badrul Sarwar, George Karypis, Joseph A. Konstan et al. · 2000 · 1.8K citations · Full text
From RankNet to LambdaRank to LambdaMART: An Overview
Christopher J. C. Burges · 2010 · 1K citations
Recommending what video to watch next
Zhe Zhao, Lichan Hong, Wei Li et al. · 2019 · 309 citations
DB2 advisor: an optimizer smart enough to recommend its own indexes
Gary Valentin, Michael J. Zuliani, Daniel C. Zilio et al. · 2002 · 290 citations
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314
Google (United States)
Mountain View, United States
7 h-index
21 publications
3.9K citations
Tsinghua University
Beijing, China
5 h-index
12 publications
477 citations
Microsoft Research Asia (China)
Beijing, China
5 h-index
15 publications
751 citations
Stanford University
Stanford, United States
5 h-index
9 publications
771 citations

Chinese Academy of Sciences
Beijing, China
5 h-index
11 publications
299 citations

Xidian University
Xi'an, China
4 h-index
16 publications
1K citations
École Nationale Supérieure d'Informatique
Algiers, Algeria
4 h-index
7 publications
185 citations
University of Science and Technology of China
Hefei, China
4 h-index
12 publications
684 citations
1–8 of 314
Repository · 11 publications · 514 citations
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10 publications · 448 citations
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5 publications · 283 citations
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5 publications · 246 citations
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5 publications · 395 citations
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