Abstract
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.
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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Cortellese, F., Nalin, M., Morandi, A., Sanna, A., Grasso, F. (2010). Personality Diagnosis for Personalized eHealth Services. In: Kostkova, P. (eds) Electronic Healthcare. eHealth 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 27. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11745-9_25
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DOI: https://doi.org/10.1007/978-3-642-11745-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11744-2
Online ISBN: 978-3-642-11745-9
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