Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
IEEE Transactions on Knowledge and Data Engineering
ST-DBSCAN: An algorithm for clustering spatial-temporal data
Data & Knowledge Engineering
Learning and inferring transportation routines
Artificial Intelligence
Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Modeling people's place naming preferences in location sharing
Proceedings of the 12th ACM international conference on Ubiquitous computing
Finding similar users using category-based location history
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information 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
A study of recommending locations on location-based social network by collaborative filtering
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Location-based and preference-aware recommendation using sparse geo-social networking data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Probabilistic sequential POIs recommendation via check-in data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Using "check-in" data gathered from location-based social networks, this paper proposes to measure the similarity of users by considering the geographical and the temporal aspect of their geographical and temporal aspects of their "check-ins". Temporal neighborhood is added to support the time dimension on the basis of the traditional DBSCAN clustering algorithm, which determines the similarity among users at different scales using the classical Vector Space Model (VSM) with vectors composed of the amount of visits in different cluster area. The spatio-temporal similarity of the user behaviors are obtained through overlapping the different weighted user similarity values. The experimental results show that the proposed approach is effective in measuring user similarity in location-based social networks.