Modelling User Preferences with Multi-Instance Genetic Programming
Amelia Zafra, Sebastián Ventura · 2008
In this paper we introduce a novel model for providing users with recommendations about web index pages of their interests. The ap-proach proposed developes user pro-files based on evolutionary multi-instance learning which determines what users find interesting and un-interesting by means of rules which add comprehensibility and clarity to user models and increase the qual-ity of the recommendations. Ex-perimental results show that our methodology achieves competitive results, providing high-quality user models which improve the accuracy of recommendations.