@conference {Mourchid2017,
	title = {Random walk based co-occurrence prediction in location-based social networks},
	booktitle = {IEEE International Conference on Communications},
	year = {2017},
	note = {cited By 0},
	abstract = {In this paper, we propose a new version of the LBRW (Learning based Random Walk), LBRW-Co, for predicting users co-occurrence based on mobility homophily and social links. More precisely, we analyze and mine jointly spatio-temporal and social features with the aim to predict and rank users co-occurrences. Experiments are performed on the Foursquare LBSN with accurate and refined measurements. Experimental results demonstrate that our LBRW-Co model have substantial advantages over baseline approaches in predicting and ranking co-occurrence interactions. {\textcopyright} 2017 IEEE.},
	doi = {10.1109/ICC.2017.7997209},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028326862\&doi=10.1109\%2fICC.2017.7997209\&partnerID=40\&md5=f865adbae7afd91e045cf307bb52ae7f},
	author = {Mourchid, F. and Kobbane, A. and Ben Othman, J. and El Koutbi, M.}
}
