Multi-instance genetic programming for web index recommendation

Dept. of Computer Sciences and Artificial Intelligence, University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain
Expert Systems with Applications (Impact Factor: 1.97). 11/2009; 36(9):11470-11479. DOI: 10.1016/j.eswa.2009.03.059

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.

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