@conference { ISI:000371484600037,
	title = {MINING USER PATTERNS FOR LOCATION PREDICTION IN MOBILE SOCIAL NETWORKS},
	booktitle = {2014 THIRD IEEE INTERNATIONAL COLLOQUIUM IN INFORMATION SCIENCE AND TECHNOLOGY (CIST{\textquoteright}14)},
	series = {Colloquium in Information Science and Technology},
	year = {2014},
	note = {3rd IEEE International Colloquium on Information Science and Technology (CIST), Tetouan, MOROCCO, OCT 20-22, 2014},
	pages = {213-218},
	publisher = {IEEE; IEEE Comp Soc; IEEE Commun Soc; IEEE Morocco Sect; IEEE Morocco Comp \& Commun Joint Chapter; UAE IEEE Student Branch; Univ Abdelmalek Essaadi; ENSA Tetouan},
	organization = {IEEE; IEEE Comp Soc; IEEE Commun Soc; IEEE Morocco Sect; IEEE Morocco Comp \& Commun Joint Chapter; UAE IEEE Student Branch; Univ Abdelmalek Essaadi; ENSA Tetouan},
	abstract = {Understanding human mobility dynamics is of an essential importance to today mobile applications, including context-aware advertising and city wide sensing applications. Recently, Location-based social networks (LBSNs) have attracted important researchers{\textquoteright} efforts, to investigate spatial, temporal and social aspects of user patterns. LBSNs allow users to {\textquoteleft}{\textquoteleft}check-in{{\textquoteright}{\textquoteright}} at geographical locations and share this information with friends. In this paper, analysis of check-ins data provided by Foursquare, the online location-based social network, allows us to construct a set of features that capture: spatial, temporal and similarity characteristics of user mobility. We apply this knowledge to location prediction problem, and combine these features in supervised learning for future location prediction. We find that the supervised classifier based on the combination of multiple features offers reasonable accuracy.},
	isbn = {978-1-4799-5979-2},
	issn = {2327-185X},
	author = {Mourchid, Fatima and Habbani, Ahmed and El koutbi, Mohamed},
	editor = {ElMohajir, M and AlAchhab, M and Chahhou, M}
}
