Detection of Points of Interest in a Smart Campus

Alessandra De Paola, Giuseppe Lo Re, Giuseppe Anastasi, Andrea Giammanco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publication2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI)
Pages155-160
Number of pages6
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Instrumentation

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  • Cite this

    De Paola, A., Lo Re, G., Anastasi, 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) (pp. 155-160)