Learning fuzzy models of user interests in a semantic information retrieval system

We propose an approach to user model-based information retrieval which uses an evolutionary algorithm to learn fuzzy models of user interests and to dynamically track their changes as the user interacts with the system. The system is ontology-based, in the sense that it considers concepts behind terms instead of simple terms. The approach has been implemented in a real-world prototype newsfeed aggregator with search facilities called IFeed. Experimental results show that our system learns user models effectively. This is proved by both the convergence of the interest degrees contained in the user models population and the increase of the users' activities on the set of proposed documents.