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.
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Acknowledgments
This work was supported by the R&D Program of MKE/KEIT.
Sincere thanks go to Mr. Jaekwon Kim who provided the idea for this thesis.
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Kim, JH., Chung, KY. (2013). Rule-Based Naive Bayesian Filtering for Personalized Recommend Service. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_117
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DOI: https://doi.org/10.1007/978-94-007-5860-5_117
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