Multi-instance genetic programming for web index recommendation

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@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