In this paper we present two different approaches to personality diagnosis, for the provision of innovative personalized services, as used in a case study where diabetic patients were supported in the improvement of physical activity in their daily life. The first approach presented relies on a static clustering of the population, with a specific motivation strategy designed for each cluster. The second approach relies on a dynamic population clustering, making use of recommendation systems and algorithms, like Collaborative Filtering. We discuss pro and cons of each approach and a possible combination of the two, as the most promising solution for this and other personalization services in eHealth. © 2010 Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering.
CITATION STYLE
Cortellese, F., Nalin, M., Morandi, A., Sanna, A., & Grasso, F. (2010). Personality diagnosis for personalized eHealth services. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (Vol. 27 LNICST, pp. 157–164). https://doi.org/10.1007/978-3-642-11745-9_25
Mendeley helps you to discover research relevant for your work.