@conference{10902/15991,
year = {2018},
url = {http://hdl.handle.net/10902/15991},
abstract = {ABSTRACT: Location data is a powerful source of information to discover user's trends and routines. A suitable identification of the user context can be exploited to provide automatically services adapted to the user preferences. In this paper, we define a Dynamic Bayesian Network model and propose a method that processes location annotated data in order to train the model. Finally, our model enables us to predict future location contexts from the user patterns. A case study evaluates the proposal using real-world data of a location-based social network.},
organization = {This research was funded by Fondo Europeo de Desarrollo Regional (FEDER) and Sociedad para el
Desarrollo Regional de Cantabria (SODERCAN) grant number TI16-IN-007 (within the program “I+C=+C 2016-
PROYECTOS DE I+D EN EL ÁMBITO DE LAS TIC, LÍNEA SMART”), and by Ministerio de Ciencia e Innovación
(MICINN), Spain grant number MTM2014-55262-P (project PAC::LFO).},
publisher = {MDPI},
publisher = {Proceedings 2018, 2(19), 1222},
title = {Discovering user's trends and routines from location based social networks},
author = {Salomón García, Sergio and Duque Medina, Rafael and Montaña Arnaiz, José Luis},
}