Multi-instance genetic programming for web index recommendation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Zafra200911470,
  author =       "A. Zafra and C. Romero and S. Ventura and 
                 E. Herrera-Viedma",
  title =        "Multi-instance genetic programming for web index
                 recommendation",
  journal =      "Expert Systems with Applications",
  volume =       "36",
  number =       "9",
  pages =        "11470--11479",
  year =         "2009",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2009.03.059",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4VXMPMD-1/2/736fb9dc8cc96734079b1b02b58a33a8",
  keywords =     "genetic algorithms, genetic programming, Multiple
                 instance learning, User modelling, Web mining",
  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