TY - GEN
T1 - Dissecting Foursquare venue popularity via random region sampling
AU - Li, Yanhua
AU - Steine, Moritz
AU - Wang, Limin
AU - Zhang, Zhi Li
AU - Bao, Jie
PY - 2012
Y1 - 2012
N2 - Location based social networks (LBSNs) are becoming increasingly popular with the fast deployment of broadband mobile networks and the growing prevalence of versatile mobile devices. This success has attracted great interest in studying and measuring the characteristics of LBSNs. However, it is often prohibitive, and sometimes impossible, to obtain a detailed and complete snapshot of a LBSN due to its usually massive scale and the lack of proper tools. In this work, we focus on sampling and estimating restricted geographic regions in LBSNs, such as cities or states, in Foursquare. By utilizing the geographic search APIs provided by Foursquare, we propose a random region sampling algorithm that allows us to draw representative samples of venues (i.e., places), and design unbiased estimators of regional characteristics of venues. Moreover, using a unique dataset with 2.4 million venues, that we collected from Foursquare, we further explore the factors affecting the venue popularity, and present our preliminary findings, with applications in venue recommendation and advertising in LBSNs.
AB - Location based social networks (LBSNs) are becoming increasingly popular with the fast deployment of broadband mobile networks and the growing prevalence of versatile mobile devices. This success has attracted great interest in studying and measuring the characteristics of LBSNs. However, it is often prohibitive, and sometimes impossible, to obtain a detailed and complete snapshot of a LBSN due to its usually massive scale and the lack of proper tools. In this work, we focus on sampling and estimating restricted geographic regions in LBSNs, such as cities or states, in Foursquare. By utilizing the geographic search APIs provided by Foursquare, we propose a random region sampling algorithm that allows us to draw representative samples of venues (i.e., places), and design unbiased estimators of regional characteristics of venues. Moreover, using a unique dataset with 2.4 million venues, that we collected from Foursquare, we further explore the factors affecting the venue popularity, and present our preliminary findings, with applications in venue recommendation and advertising in LBSNs.
KW - Foursquare
KW - Location based social networks
KW - Sampling
UR - http://www.scopus.com/inward/record.url?scp=84871951391&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871951391&partnerID=8YFLogxK
U2 - 10.1145/2413247.2413261
DO - 10.1145/2413247.2413261
M3 - Conference contribution
AN - SCOPUS:84871951391
SN - 9781450317757
T3 - CoNEXT Student 2012 - Proceedings of the ACM Conference on the 2012 CoNEXT Student Workshop
SP - 21
EP - 22
BT - CoNEXT Student 2012 - Proceedings of the ACM Conference on the 2012 CoNEXT Student Workshop
T2 - 2012 ACM CoNEXT Student Workshop, CoNEXT Student 2012
Y2 - 10 December 2012 through 10 December 2012
ER -