Finding similar users using category-based location history
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Learning travel recommendations from user-generated GPS traces
ACM Transactions on Intelligent Systems and Technology (TIST)
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
SeMiTri: a framework for semantic annotation of heterogeneous trajectories
Proceedings of the 14th International Conference on Extending Database Technology
A novel frequent trajectory mining method based on GSP
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part I
Towards geosocial recommender systems
Proceedings of the 4th International Workshop on Web Intelligence & Communities
The preface of the 4th International Workshop on Location-Based Social Networks
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
LBSNRank: personalized pagerank on location-based social networks
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
An object based conceptual framework for location based social networking
Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
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People’s location histories imply the location correlation that states the relations between geographical locations in the space of human behavior. With the correlation, we can enable many valuable services, such as location recommendation and sales promotion. In this paper, by taking into account a user’s travel experience (knowledge) and the sequentiality that locations have been visited, we learn the location correlation from a large number of user-generated GPS trajectories. Using the location correlation, we conduct a personalized location recommendation system, which is evaluated based on a real-world GPS dataset collected by 112 users over a period of 1.5 years. As a result, our method outperforms that using the Pearson correlation.