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EnglishUnderstanding users' habits is a critical task in order to develop advanced services, such as personalized recommendation and virtual assistance. In this work, we propose a novel approach to detect Points of Interest visited by users of a campus, by using mobility traces collected through users' smartphones. Our method takes advantage of the intentional and recurrent nature of human movements to build up mobility profiles, and combines different machine learning methods to merge sensory information with the past users' behavior. The proposed approach has been validated on a synthetic dataset and the experimental results show its effectiveness.
de Paola, A., Giammanco, A., lo Re, G., & Anastasi, G. (2019). Detection of Points of Interest in a Smart Campus. In 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI) (pp. 155-160). Institute of Electrical and Electronics Engineers Inc. [10.1109/RTSI.2019.8895569].
| Data di pubblicazione: | 2019 | |
| Titolo: | Detection of Points of Interest in a Smart Campus | |
| Autori: | ||
| Citazione: | de Paola, A., Giammanco, A., lo Re, G., & Anastasi, G. (2019). Detection of Points of Interest in a Smart Campus. In 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI) (pp. 155-160). Institute of Electrical and Electronics Engineers Inc. [10.1109/RTSI.2019.8895569]. | |
| Abstract: | Understanding users' habits is a critical task in order to develop advanced services, such as personalized recommendation and virtual assistance. In this work, we propose a novel approach to detect Points of Interest visited by users of a campus, by using mobility traces collected through users' smartphones. Our method takes advantage of the intentional and recurrent nature of human movements to build up mobility profiles, and combines different machine learning methods to merge sensory information with the past users' behavior. The proposed approach has been validated on a synthetic dataset and the experimental results show its effectiveness. | |
| ISBN: | 9781728138152 | |
| Digital Object Identifier (DOI): | 10.1109/RTSI.2019.8895569 | |
| Settore Scientifico Disciplinare: | Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni | |
| Appare nelle tipologie: | 2.07 Contributo in atti di convegno pubblicato in volume |
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