Listwise Ranking Models
Listwise Ranking Models is a type of learning-to-rank framework that treats ranking as a holistic optimization problem by directly modeling the permutation of an entire document list with respect to a query, thereby distinguishing itself from pointwise and pairwise approaches through its global consideration of list-level structure and relevance dependencies.
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6.2K
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2017–2026
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Listwise Learning-to-rank Models
Parents
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Learning to rank for information retrieval
Tie‐Yan Liu · 2010 · 2K citations
Listwise approach to learning to rank
Fen Xia, Tie‐Yan Liu, Jue Wang et al. · 2008 · 701 citations
Learning a Deep Listwise Context Model for Ranking Refinement
2018 · 131 citations · Full text
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Chinese Academy of Sciences
Beijing, China
9 h-index
29 publications
2.9K citations
Microsoft Research Asia (China)
Beijing, China
5 h-index
8 publications
4.4K citations

Zhejiang University
Hangzhou, China
3 h-index
19 publications
643 citations
Institute of Computing Technology
Beijing, China
3 h-index
12 publications
387 citations
Yahoo (United States)
Sunnyvale, United States
2 h-index
5 publications
221 citations

University of Massachusetts Amherst
Amherst Center, United States
2 h-index
6 publications
435 citations

South China University of Technology
Guangzhou, China
2 h-index
4 publications
73 citations
Microsoft (United States)
Redmond, United States
2 h-index
3 publications
813 citations
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Repository · 2 publications · 46 citations
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2 publications · 802 citations
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Conference · 1 publications · 17 citations
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1 publications · 14 citations
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