Multi-instance genetic programming for web index recommendation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Zafra200911470,
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author = "A. Zafra and C. Romero and S. Ventura and
E. Herrera-Viedma",
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title = "Multi-instance genetic programming for web index
recommendation",
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journal = "Expert Systems with Applications",
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volume = "36",
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number = "9",
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pages = "11470--11479",
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year = "2009",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2009.03.059",
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URL = "http://www.sciencedirect.com/science/article/B6V03-4VXMPMD-1/2/736fb9dc8cc96734079b1b02b58a33a8",
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keywords = "genetic algorithms, genetic programming, Multiple
instance learning, User modelling, Web mining",
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abstract = "This article introduces the use of a multi-instance
genetic programming algorithm for modelling user
preferences in web index recommendation systems. The
developed algorithm learns user interest by means of
rules which add comprehensibility and clarity to the
discovered models and increase the quality of the
recommendations. This new model, called G3P-MI
algorithm, is evaluated and compared with other
available algorithms. Computational experiments show
that our methodology achieves competitive results and
provide high-quality user models which improve the
accuracy of recommendations.",
- }
Genetic Programming entries for
Amelia Zafra Gomez
Cristobal Romero Morales
Sebastian Ventura
Enrique Herrera Viedma
Citations