Dissecting foursquare venue popularity via random region sampling

Authors:
Yanhua Li;Moritz Steiner;Limin Wang;Zhi-Li Zhang;Jie Bao
Affiliations:
University of Minnesota, Twin Cities, Minneapolis, MN, USA;Bell labs, Murray Hill, NJ, USA;Bell Labs, Murray Hill, NJ, USA;University of Minnesota, Twin Cities, Minneapolis, MN, USA;University of Minnesota, Twin Cities, Minneapolis, MN, USA
Venue:
Proceedings of the 2012 ACM conference on CoNEXT student workshop
Year:
2012

Citing 2
Cited 0

Counting YouTube videos via random prefix sampling

Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Location-based and preference-aware recommendation using sparse geo-social networking data

Proceedings of the 20th International Conference on Advances in Geographic Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.