Belief revision and text mining for adaptive recommender agents

Abstract

With the rapid growth of the number of electronic transactions conducted over the Internet, recommender systems have been proposed to provide consumers with personalized product recommendations. This paper illustrates how belief revision and text mining can be used to improve recommender agents’ prediction effectiveness, learning autonomy, adaptiveness, and explanatory capabilities. To our best knowledge, this is the first study of integrating text mining techniques and belief revision logic into a single framework for the development of adaptive recommender agents.