Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Mobile marketing: the role of permission and acceptance
International Journal of Mobile Communications
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Harnessing the Crowdsourcing Power of Social Media for Disaster Relief
IEEE Intelligent Systems
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
LCARS: a location-content-aware recommender system
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling temporal effects of human mobile behavior on location-based social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Exploring temporal effects for location recommendation on location-based social networks
Proceedings of the 7th ACM conference on Recommender systems
iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Where you like to go next: successive point-of-interest recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Location-based social networks (LBSNs) have attracted an increasing number of users in recent years. The availability of geographical and social information of online LBSNs provides an unprecedented opportunity to study the human movement from their socio-spatial behavior, enabling a variety of location-based services. Previous work on LBSNs reported limited improvements from using the social network information for location prediction; as users can check-in at new places, traditional work on location prediction that relies on mining a user's historical trajectories is not designed for this "cold start" problem of predicting new check-ins. In this paper, we propose to utilize the social network information for solving the "cold start" location prediction problem, with a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance. The experimental results on a real-world LBSN demonstrate that our approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures.