Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Developing privacy guidelines for social location disclosure applications and services
SOUPS '05 Proceedings of the 2005 symposium on Usable privacy and security
Discovering personally meaningful places: An interactive clustering approach
ACM Transactions on Information Systems (TOIS)
From awareness to repartee: sharing location within social groups
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Going my way: a user-aware route planner
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining user similarity from semantic trajectories
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Finding similar users using category-based location history
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
A system for destination and future route prediction based on trajectory mining
Pervasive and Mobile Computing
A location based reminder system for advertisement
Proceedings of the international conference on Multimedia
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
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A place is a locale that is frequently visited by an individual user and carries important semantic meanings (e.g. home, work, etc.). Many location-aware applications will be greatly enhanced with the ability of the automatic discovery of personally semantic places. The discovery of a user's personally semantic places involves obtaining the physical locations and semantic meanings of these places. In this paper, we propose approaches to address both of the problems. For the physical place extraction problem, a hierarchical clustering algorithm is proposed to firstly extract visit points from the GPS trajectories, and then these visit points can be clustered to form physical places. For the semantic place recognition problem, Bayesian networks (encoding the temporal patterns in which the places are visited) are used in combination with a customized POI (i.e. place of interest) database (containing the spatial features of the places) to categorize the extracted physical places into pre-defined types. An extensive set of experiments have been conducted to demonstrate the effectiveness of the proposed approaches based on a dataset of real-world GPS trajectories.