PROCEEDINGS IPMU '08
Modelling User Preferences with Multi-Instance Genetic Programming
Amelia Zafra, Sebastián Ventura
In this paper we introduce a novel
model for providing users with
recommendations about web index
pages of their interests. The approach
proposed developes user profiles
based on evolutionary multiinstance
learning which determines
what users find interesting and uninteresting
by means of rules which
add comprehensibility and clarity to
user models and increase the quality
of the recommendations. Experimental
results show that our
methodology achieves competitive
results, providing high-quality user
models which improve the accuracy
of recommendations.
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