Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm
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- @InProceedings{He:2010:CIKM,
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author = "Qiang He and Jun Ma and Shuaiqiang Wang",
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title = "Directly optimizing evaluation measures in learning to
rank based on the clonal selection algorithm",
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booktitle = "Proceedings of the 19th ACM international conference
on Information and knowledge management, CIKM '10",
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year = "2010",
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pages = "1449--1452",
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address = "Toronto, ON, Canada",
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, clonal
selection algorithm, information retrieval, learning to
rank, machine learning, ranking function: Poster",
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isbn13 = "978-1-4503-0099-5",
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DOI = "doi:10.1145/1871437.1871644",
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size = "4 pages",
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acmid = "1871644",
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abstract = "One fundamental issue of learning to rank is the
choice of loss function to be optimised. Although the
evaluation measures used in Information Retrieval (IR)
are ideal ones, in many cases they can't be used
directly because they do not satisfy the smooth
property needed in conventional machine learning
algorithms. In this paper a new method named RankCSA is
proposed, which tries to use IR evaluation measure
directly. It employs the clonal selection algorithm to
learn an effective ranking function by combining
various evidences in IR. Experimental results on the
LETOR benchmark datasets demonstrate that RankCSA
outperforms the baseline methods in terms of P@n, MAP
and NDCG@n.",
- }
Genetic Programming entries for
Qiang He
Jun Ma
Shuaiqiang Wang
Citations