Applying layered multi-population genetic programming on learning to rank for information retrieval

Information retrieval (IR) returns a relative ranking of documents with respect to a user query. Learning to rank for information retrieval (LR4IR) employs supervised learning techniques to address this problem, and it aims to produce a ranking model automatically for defining a proper sequential or...

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Bibliographic Details
Published in2012 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 1754 - 1759
Main Authors Jung Yi Lin, Jen-Yuan Yeh, Chao-Chung Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2012
Subjects
Abstracts
Evolutionary computation
Genetic programming
Learning to rank for Information Retrieval
Programming
Ranking function
Sociology
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Summary:Information retrieval (IR) returns a relative ranking of documents with respect to a user query. Learning to rank for information retrieval (LR4IR) employs supervised learning techniques to address this problem, and it aims to produce a ranking model automatically for defining a proper sequential order of related documents based on the query. The ranking model determines the relationship degree between documents and the query. In this paper an improved version of RankGP is proposed. It uses layered multi-population genetic programming to obtain a ranking function which consists of a set of IR evidences and particular predefined operators. The proposed method is capable to generate complex functions through evolving small populations. In this paper, LETOR 4.0 was used to evaluate the effectiveness of the proposed method and the results showed that the method is competitive with other LR4IR Algorithms.
ISBN:1467314846
9781467314848
ISSN:2160-133X
DOI:10.1109/ICMLC.2012.6359640