Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Space-time dynamics of topics in streaming text
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Finding your friends and following them to where you are
Proceedings of the fifth ACM international conference on Web search and data mining
City-scale traffic simulation from digital footprints
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Characterizing Urban Landscapes Using Geolocated Tweets
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Location-based and preference-aware recommendation using sparse geo-social networking data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Mining User Mobility Features for Next Place Prediction in Location-Based Services
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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Location-based social networks serve as a source of data for a wide range of applications, from recommendation of places to visit to modelling of city traffic, and urban planning. One of the basic problems in all these areas is the formulation of a predictive model for the location of a certain user at a certain time. In this paper, we propose a new approach for predicting user location, which uses two components to make the prediction, based on (i) coordinates and times of user check-ins and (ii) social interaction between different users. We improve the performance of a state-of-the art model using the radiation model of spatial choice and a social component based on the frequency of matching check-ins of user's friends. Friendship is defined by the presence of reciprocal following on Twitter. Our empirical results highlight an improvement over the state-of-the-art in terms of accuracy, and suggest practical solutions for spatio-temporal and socially-inspired prediction of user location.