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User identification from mobility traces

Abstract: Geolocation is a powerful source of information through which user patterns can be extracted. User regions-of-interest, along with these patterns, can be used to recognize and imitate user behavior. In this work we develop a methodology for preprocessing location data in order to discover the most relevant places the user visits, and we propose a Probabilistic Finite Automaton structure as mobility model. We analyse both location prediction and user identification tasks. Our model is assessed with two evaluation metrics regarding its predictive accuracy and user identification accuracy, and compared against other models.

 Fuente: Journal of Ambient Intelligence and Humanized Computing, 2023, 14, 31-40

 Editorial: Springer

 Año de publicación: 2023

 Nº de páginas: 10

 Tipo de publicación: Artículo de Revista

 DOI: 10.1007/s12652-018-1117-4

 ISSN: 1868-5145,1868-5137

 Proyecto español: MTM2014-55262-P

 Url de la publicación: https://doi.org/10.1007/s12652-018-1117-4