Document ontology based personalized filtering system

Abstract

We propose the use of the personalized ontology model to improve the effectiveness of web documents filtering process. One important feature of this model is that by constructing the user specific ontology, web documents can be classified by using the user oriented meta data that reflects the user's view about the documents concept. Another is that by applying the user model to searching the classified documents, we achieved the effective document search performance. To find the user's preference, Bayesian Learner accepts user's interests flow as an input and writes output to users profile. Based on those user profiles, user specific ontologies are constructed to provide efficient search environment.