Learning to Rank is a machine learning methodology within information retrieval that systematically trains ranking functions to order search results by leveraging labeled relevance judgments, thereby distinguishing itself from traditional heuristic or static ranking approaches through its data-driven, supervised optimization of retrieval effectiveness.
902
Publications
66.5K
Citations
2.1K
Authors
594
Institutions
Publications per year
2017–2026
324
Publications
902
Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw et al. · 2005 · 2.8K citations
Zhe Cao, Tao Qin, Tie‐Yan Liu et al. · 2007 · 2K citations
Learning to rank for information retrieval
Tie‐Yan Liu · 2010 · 2K citations
1–5 of 902
Authors
2.1K
- MB
18
h-index
- MD
17
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- XW
17
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- TL
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- MN
15
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1–5 of 2.1K
Institutions
594
Microsoft (United States)
Redmond, United States
40 h-index
113 publications
35.1K citations
Microsoft Research Asia (China)
Beijing, China
37 h-index
101 publications
22.8K citations
Google (United States)
Mountain View, United States
25 h-index
146 publications
10.2K citations
Chinese Academy of Sciences
Beijing, China
22 h-index
100 publications
7.5K citations
University of Amsterdam
Amsterdam, The Netherlands
22 h-index
82 publications
3.1K citations
Carnegie Mellon University
Pittsburgh, United States
21 h-index
50 publications
5.1K citations
Tsinghua University
Beijing, China
20 h-index
56 publications
8.1K citations
Yahoo (United States)
Sunnyvale, United States
20 h-index
90 publications
5.6K citations
1–8 of 594
Venues
- arXiv (Cornell University)
Repository · 43 publications · 1.4K citations
20
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- ACM Transactions on Information Systems
13 publications · 487 citations
12
h-index
- IEEE Transactions on Knowledge and Data Engineering
TKDE · 12 publications · 423 citations
12
h-index
- Information Processing & Management
11 publications · 524 citations
11
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- Information Retrieval
10 publications · 1.1K citations
10
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1–5