Detection of Points of Interest in a Smart Campus

Risultato della ricerca: Conference contribution

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.
Lingua originaleEnglish
Titolo della pubblicazione ospite2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI)
Pagine155-160
Numero di pagine6
Stato di pubblicazionePublished - 2019

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Smartphones
Learning systems

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De Paola, A., Lo Re, G., & Giammanco, A. (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) (pagg. 155-160)

Detection of Points of Interest in a Smart Campus. / De Paola, Alessandra; Lo Re, Giuseppe; Giammanco, Andrea.

2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI). 2019. pag. 155-160.

Risultato della ricerca: Conference contribution

De Paola, A, Lo Re, G & Giammanco, A 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). pagg. 155-160.
De Paola A, Lo Re G, Giammanco A. Detection of Points of Interest in a Smart Campus. In 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI). 2019. pag. 155-160
De Paola, Alessandra ; Lo Re, Giuseppe ; Giammanco, Andrea. / Detection of Points of Interest in a Smart Campus. 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI). 2019. pagg. 155-160
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AB - 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.

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