Rule-Based Naive Bayesian Filtering for Personalized Recommend Service

Abstract

The recommendation of u-Health personalized service in a semantic environment should be done only after evaluating individual physical health conditions and illnesses. The existing recommendation method of u-Health personalized service in a semantic environment had low user satisfaction because its recommendation was dependent on ontology for analyzing significance. Thus, this article suggests a personalized service recommendation method based on Naive Bayesian Classifier for u-Health service in a semantic environment. In accordance with the suggested method, the condition data are inferred by using ontology, and the transaction is saved. By applying a Naive Bayesian Classifier that uses preference information, the service is provided based on user preference information and transactions formed from ontology. The service based on the Naive Bayesian Classifier shows a higher accuracy and recall ratio of the contents recommendation than the existing method.