Random walk based co-occurrence prediction in location-based social networks

TitreRandom walk based co-occurrence prediction in location-based social networks
Publication TypeConference Paper
Year of Publication2017
AuthorsMourchid, F, Kobbane, A, Ben Othman, J, M. Koutbi, E
Conference NameIEEE International Conference on Communications
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. © 2017 IEEE.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85028326862&doi=10.1109%2fICC.2017.7997209&partnerID=40&md5=f865adbae7afd91e045cf307bb52ae7f
DOI10.1109/ICC.2017.7997209
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