Harnessing the Crowdsourcing Power of Social Media for Disaster Relief
IEEE Intelligent Systems
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
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
eTrust: understanding trust evolution in an online world
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
gSCorr: modeling geo-social correlations for new check-ins on location-based social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Checking in or checked in: comparing large-scale manual and automatic location disclosure patterns
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Hi-index | 0.00 |
The rapid growth of location-based social networks (LBSNs) invigorates an increasing number of LBSN users, providing an unprecedented opportunity to study human mobile behavior from spatial, temporal, and social aspects. Among these aspects, temporal effects offer an essential contextual cue for inferring a user's movement. Strong temporal cyclic patterns have been observed in user movement in LBSNs with their correlated spatial and social effects (i.e., temporal correlations). It is a propitious time to model these temporal effects (patterns and correlations) on a user's mobile behavior. In this paper, we present the first comprehensive study of temporal effects on LBSNs. We propose a general framework to exploit and model temporal cyclic patterns and their relationships with spatial and social data. The experimental results on two real-world LBSN datasets validate the power of temporal effects in capturing user mobile behavior, and demonstrate the ability of our framework to select the most effective location prediction algorithm under various combinations of prediction models.