Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Learning transportation mode from raw gps data for geographic applications on the web
Proceedings of the 17th international conference on World Wide Web
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
GeoLife2.0: A Location-Based Social Networking Service
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Mining trajectory profiles for discovering user communities
Proceedings of the 2009 International Workshop on Location Based Social Networks
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Learning Location Correlation from GPS Trajectories
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
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)
Mining mobility user profiles for car pooling
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining significant time intervals for relationship detection
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Collaborative activity recognition via check-in history
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
A habit mining approach for discovering similar mobile users
Proceedings of the 21st international conference on World Wide Web
Identifying users profiles from mobile calls habits
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Characterizing large-scale population's indoor spatio-temporal interactive behaviors
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
User oriented trajectory similarity search
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
TraMSNET: a mobile social network application for tourism
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Discovering personally semantic places from GPS trajectories
Proceedings of the 21st ACM international conference on Information and knowledge management
Generating tourism path from trajectories and geo-photos
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Constructing and comparing user mobility profiles for location-based services
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Finding frequent sub-trajectories with time constraints
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
Geographical and temporal similarity measurement in location-based social networks
Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Mining geographic-temporal-semantic patterns in trajectories for location prediction
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Hi-index | 0.00 |
In this paper, we aim to estimate the similarity between users according to their GPS trajectories. Our approach first models a user's GPS trajectories with a semantic location history (SLH), e.g., shopping malls → restaurants → cinemas. Then, we measure the similarity between different users' SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user's interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. As a result, SLH-MTM outperforms the related works [4].