Paper Info
Title
Beyond \"local\", \"categories\" and \"friends\": clustering foursquare users with latent \"topics\"
Abstract
In this work, we use foursquare check-ins to cluster users via topic modeling, a technique commonly used to classify text documents according to latent \"themes\". Here, however, the latent variables which group users can be thought of not as themes but rather as factors which drive check in behaviors, allowing for a qualitative understanding of influences on user check ins. Our model is agnostic of geo-spatial location, time, users' friends on social networking sites and the venue categories-we treat the existence of and intricate interactions between these factors as being latent, allowing them to emerge entirely from the data. We instantiate our model on data from New York and the San Francisco Bay Area and find evidence that the model is able to identify groups of people which are of different types (e.g. tourists), communities (e.g. users tightly clustered in space) and interests (e.g. people who enjoy athletics).
Year
DOI
Venue
2012
10.1145/2370216.2370422
UbiComp
Keywords
Field
DocType
cluster user,intricate interaction,user check in,group user,foursquare user,geo-spatial location,san francisco bay area,different type,latent variable,foursquare check-ins,new york,location based service,topic modeling
Social group,World Wide Web,Social network,Check-in,Computer science,Location-based service,Latent variable,Topic model,Cluster analysis
Conference
Citations 
PageRank 
References 
30
1.41
15
Authors
3
Name
Order
Citations
PageRank
Kenneth Joseph1709.46
Chun How Tan2502.53
Kathleen M. Carley32507270.10