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Amazon.com Recommendations: Item-to-Item Collaborative Filtering
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Evaluating similarity measures: a large-scale study in the orkut social network
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Using Location for Personalized POI Recommendations in Mobile Environments
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A location-aware recommender system for mobile shopping environments
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Learning transportation mode from raw gps data for geographic applications on the web
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Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
CityVoyager: an outdoor recommendation system based on user location history
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Mobility detection using everyday GSM traces
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Predestination: inferring destinations from partial trajectories
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Mining interesting locations and travel sequences from GPS trajectories
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Mining trajectory profiles for discovering user communities
Proceedings of the 2009 International Workshop on Location Based Social Networks
Joint learning user's activities and profiles from GPS data
Proceedings of the 2009 International Workshop on Location Based Social Networks
From trajectories to activities: a spatio-temporal join approach
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Mining correlation between locations using human location history
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Collaborative location and activity recommendations with GPS history data
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Bridging the gap between physical location and online social networks
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Mining user similarity from semantic trajectories
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Querying geo-social data by bridging spatial networks and social networks
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Finding similar users using category-based location history
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Time geography inverted: recognizing intentions in space and time
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SensLoc: sensing everyday places and paths using less energy
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Learning travel recommendations from user-generated GPS traces
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Recommending friends and locations based on individual location history
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A user similarity calculation based on the location for social network services
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A framework of mining semantic regions from trajectories
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CLR: a collaborative location recommendation framework based on co-clustering
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Protecting location privacy using location semantics
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Mining significant time intervals for relationship detection
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Unveiling the complexity of human mobility by querying and mining massive trajectory data
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NNCluster: an efficient clustering algorithm for road network trajectories
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Mining user similarity based on routine activities
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Semantic trajectories: Mobility data computation and annotation
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Unified entity search in social media community
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Weighted multi-attribute matching of user-generated points of interest
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Compact representation of GPS trajectories over vectorial road networks
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The pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enable people to conveniently log the location histories they visited with spatio-temporal data. The increasing availability of large amounts of spatio-temporal data pertaining to an individual's trajectories has given rise to a variety of geographic information systems, and also brings us opportunities and challenges to automatically discover valuable knowledge from these trajectories. In this paper, we move towards this direction and aim to geographically mine the similarity between users based on their location histories. Such user similarity is significant to individuals, communities and businesses by helping them effectively retrieve the information with high relevance. A framework, referred to as hierarchical-graph-based similarity measurement (HGSM), is proposed for geographic information systems to consistently model each individual's location history and effectively measure the similarity among users. In this framework, we take into account both the sequence property of people's movement behaviors and the hierarchy property of geographic spaces. We evaluate this framework using the GPS data collected by 65 volunteers over a period of 6 months in the real world. As a result, HGSM outperforms related similarity measures, such as the cosine similarity and Pearson similarity measures.