Collaborative filtering meets next check-in location prediction
Proceedings of the 22nd international conference on World Wide Web companion
Acquaintance or partner?: predicting partnership in online and location-based social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
A HITS-based POI recommendation algorithm for location-based social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Personalized point-of-interest recommendation by mining users' preference transition
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
LearNext: learning to predict tourists movements
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Prediction of user location using the radiation model and social check-ins
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
Redeem with privacy (RWP): privacy protecting framework for geo-social commerce
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
On the validity of geosocial mobility traces
Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks
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Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatio-temporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this work we study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. We first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months. We then propose a set of features that aim to capture the factors that may drive users' movements. Our features exploit information on transitions between types of places, mobility flows between venues, and spatio-temporal characteristics of user check-in patterns. We further extend our study combining all individual features in two supervised learning models, based on linear regression and M5 model trees, resulting in a higher overall prediction accuracy. We find that the supervised methodology based on the combination of multiple features offers the highest levels of prediction accuracy: M5 model trees are able to rank in the top fifty venues one in two user check-ins, amongst thousands of candidate items in the prediction list.