Parallel Learning to Rank for Information Retrieval
Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for cont...
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Institutional Knowledge at Singapore Management University
2011
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sg-smu-ink.sis_research-25162017-12-26T10:15:48Z Parallel Learning to Rank for Information Retrieval WANG, Shuaiqiang GAO, Byron J. WANG, Ke LAUW, Hady W. Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency. 2011-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1517 info:doi/10.1145/2009916.2010060 https://ink.library.smu.edu.sg/context/sis_research/article/2516/viewcontent/sigir11.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University learning to rank mapreduce parallel algorithms information retrieval cooperative coevolution Databases and Information Systems |
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SMU Libraries |
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Singapore Singapore |
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learning to rank mapreduce parallel algorithms information retrieval cooperative coevolution Databases and Information Systems |
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learning to rank mapreduce parallel algorithms information retrieval cooperative coevolution Databases and Information Systems WANG, Shuaiqiang GAO, Byron J. WANG, Ke LAUW, Hady W. Parallel Learning to Rank for Information Retrieval |
| description |
Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficiency is becoming an important issue due to the unprecedented availability of large-scale training data and the need for continuous update of ranking functions. In this paper, we investigate parallel learning to rank, targeting simultaneous improvement in accuracy and efficiency. |
| format |
text |
| author |
WANG, Shuaiqiang GAO, Byron J. WANG, Ke LAUW, Hady W. |
| author_facet |
WANG, Shuaiqiang GAO, Byron J. WANG, Ke LAUW, Hady W. |
| author_sort |
WANG, Shuaiqiang |
| title |
Parallel Learning to Rank for Information Retrieval |
| title_short |
Parallel Learning to Rank for Information Retrieval |
| title_full |
Parallel Learning to Rank for Information Retrieval |
| title_fullStr |
Parallel Learning to Rank for Information Retrieval |
| title_full_unstemmed |
Parallel Learning to Rank for Information Retrieval |
| title_sort |
parallel learning to rank for information retrieval |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
2011 |
| url |
https://ink.library.smu.edu.sg/sis_research/1517 https://ink.library.smu.edu.sg/context/sis_research/article/2516/viewcontent/sigir11.pdf |
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1770571215953461248 |