Evaluating location predictors with extensive Wi-Fi mobility data
ACM SIGMOBILE Mobile Computing and Communications Review
Location prediction algorithms for mobile wireless systems
Wireless internet handbook
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On the semantic annotation of places in 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
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Learning location naming from user check-in histories
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap
Proceedings of the 9th International Symposium on Open Collaboration
| Hi-index | 0.00 |
Understanding the patterns underlying human mobility is of an essential importance to applications like recommender systems. In this paper we investigate the behaviour of around 10,000 frequent users of Location Based Social Networks (LBSNs) making use of their full movement patterns. We analyse the metadata associated with the whereabouts of the users, with emphasis on the type of places and their evolution over time. We uncover patterns across different temporal scales for venue category usage. Then, focusing on individual users, we apply this knowledge in two tasks: 1) clustering users based on their behaviour and 2) predicting users' future movements. By this, we demonstrate both qualitatively and quantitatively that incorporating temporal regularities is beneficial for making better sense of user behaviour.