Learning to Rank for Information Retrieval Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Yeh:2007:SIGIR,
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author = "Jen-Yuan Yeh and Jung-Yi Lin and Hao-Ren Ke and
Wei-Pang Yang",
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title = "Learning to Rank for Information Retrieval Using
Genetic Programming",
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booktitle = "SIGIR 2007 workshop: Learning to Rank for Information
Retrieval",
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year = "2007",
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editor = "Thorsten Joachims and Hang Li and Tie-Yan Liu and
ChengXiang Zhai",
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month = "27 " # jul,
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organisation = "Microsoft",
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keywords = "genetic algorithms, genetic programming, learning to
rank for IR, ranking function, Information Storage and
Retrieval, Information Search and Retrieval, Retrieval
Models, Algorithms, Experimentation, Performance",
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URL = "http://jenyuan.yeh.googlepages.com/jyyeh-LR4IR07.pdf",
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size = "8 pages",
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abstract = "One central problem of information retrieval (IR) is
to determine which documents are relevant and which are
not to the user information need. This problem is
practically handled by a ranking function which defines
an ordering among documents according to their degree
of relevance to the user query. This paper discusses
work on using machine learning to automatically
generate an effective ranking function for IR. This
task is referred to as learning to rank for IR in the
field. In this paper, a learning method, RankGP, is
presented to address this task. RankGP employs genetic
programming to learn a ranking function by combining
various types of evidences in IR, including content
features, structure features, and query-independent
features. The proposed method is evaluated using the
LETOR benchmark datasets and found to be competitive
with Ranking SVM and RankBoost.",
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notes = "broken Jun 2023
https://research.microsoft.com/en-us/um/beijing/events/LR4IR-2007/",
- }
Genetic Programming entries for
Jen-Yuan Yeh
Mick Jung-Yi Lin
Hao-Ren Ke
Wei-Pang Yang
Citations