Listwise Ranking
Listwise Ranking is a learning-to-rank approach that treats ranking as a holistic prediction task by directly optimizing the entire list of ordered items as a single instance, thereby differing from pairwise or pointwise methods that decompose ranking into simpler classification or regression subproblems.
51
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3.2K
Citations
182
Authors
74
Institutions
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2017–2026
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Listwise approach to learning to rank
Fen Xia, Tie‐Yan Liu, Jue Wang et al. · 2008 · 701 citations
Boosting coverage-based fault localization via graph-based representation learning
Yiling Lou, Qihao Zhu, Jinhao Dong et al. · 2021 · 137 citations
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Chinese Academy of Sciences
Beijing, China
7 h-index
24 publications
2.5K citations

Zhejiang University
Hangzhou, China
4 h-index
24 publications
982 citations
Microsoft Research Asia (China)
Beijing, China
4 h-index
7 publications
1K citations

University of Electronic Science and Technology of China
Chengdu, China
2 h-index
3 publications
1.6K citations
Institute of Computing Technology
Beijing, China
2 h-index
8 publications
115 citations
University of California San Diego
San Diego, United States
2 h-index
5 publications
77 citations
Yahoo (United States)
Sunnyvale, United States
2 h-index
4 publications
126 citations
The University of Texas at Austin
Austin, United States
2 h-index
5 publications
212 citations
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Repository · 4 publications · 72 citations
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Conference · 2 publications · 28 citations
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2 publications · 802 citations
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1 publications · 14 citations
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1 publications · 14 citations
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