Learning to rank for information retrieval using layered multi-population genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Lin:2012:CyberneticsCom,

• author = "Jung Yi Lin and Jen-Yuan Yeh and Chao Chung Liu",
• booktitle = "IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom 2012)",
• title = "Learning to rank for information retrieval using layered multi-population genetic programming",
• year = "2012",
• pages = "45--49",
• DOI = "doi:10.1109/CyberneticsCom.2012.6381614",
• size = "5 pages",
• abstract = "To determine which documents are relevant and which are not to the user query is one central problem broadly studied in the field of information retrieval (IR). Learning to rank for information retrieval (LR4IR), which leverages supervised learning-based methods to address the problem, aims to produce a ranking model automatically for defining a proper sequential order of related documents according to the given query. The ranking model is employed to determine the relationship degree between one document and the user query, based on which a ranking of query-related documents could be produced. In this paper we proposed an improved RankGP algorithm using multi-layered multi-population genetic programming to obtain a ranking function, trained from collections of IR results with relevance judgements. In essence, the generated ranking function is consisted of a set of IR evidences (or features) and particular predefined GP operators. The proposed method is capable of generating complex functions through evolving small populations. LETOR 4.0 was used to evaluate the effectiveness of the proposed method and the results showed that the method is competitive with RankSVM and AdaRank.",
• keywords = "genetic algorithms, genetic programming, document handling, learning (artificial intelligence), query processing, AdaRank, GP operator, LETOR 4.0, LR4IR, RankGP algorithm, RankSVM, learning to rank for information retrieval, miltilayered multipopulation genetic programming, query-related document, ranking model, relevance judgment, supervised learning, support vector machines, user query, Feature extraction, Information retrieval, Machine learning, Sociology, Statistics, Training, Vectors, Learning to rank for Information Retrieval, evolutionary computation, ranking function",
• notes = "Also known as \cite{6381614}",

}

Genetic Programming entries for Mick Jung-Yi Lin Jen-Yuan Yeh Chao Chung Liu

Citations