Classification based on association rules for adaptive web systems

Abstract

The main objective of this work is to apply more effective methods than the traditional supervised techniques in the implementation of personalized recommender systems, which improve the accuracy of the predictions in classification tasks. Different model-based classification algorithms based on association rules and others that combine the induction of decision trees with this type of rule were studied. Data from the MovieLens recommender system was used in the analysis and comparison of the different algorithms. © 2007 Springer-Verlag Berlin Heidelberg.