The Journal of Machine Learning Research
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Crowd-based urban characterization: extracting crowd behavioral patterns in urban areas from Twitter
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
LPTA: A Probabilistic Model for Latent Periodic Topic Analysis
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Finding your friends and following them to where you are
Proceedings of the fifth ACM international conference on Web search and data mining
Trade area analysis using user generated mobile location data
Proceedings of the 22nd international conference on World Wide Web
Flarty: recommending art routes using check-ins latent topics
Proceedings of the 21st ACM international conference on Multimedia
Hi-index | 0.00 |
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).