@article{10902/31393,
year = {2018},
url = {https://hdl.handle.net/10902/31393},
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.},
organization = {The authors gratefully acknowledge the financial support from FEDER (Fondo Europeo de Desarrollo Regional) and SODERCAN (Sociedad para el Desarrollo Regional de Cantabria) for the project TI16-IN-007 within the program "I+C=+C 2016—PROYECTOS DE I+D EN EL ÁMBITO DE LAS TIC, LÍNEA SMART", and from Ministerio de Ciencia e Innovación (MICINN), Spain for the project PAC::LFO (MTM2014-55262-P).},
publisher = {Springer},
publisher = {Journal of Ambient Intelligence and Humanized Computing, 2023, 14, 31-40},
title = {User identification from mobility traces},
author = {Salomón, Sergio and Tirnauca, Cristina and Duque Medina, Rafael and Montaña Arnaiz, José Luis},
}