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  1. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks (LBSN '10)
  2. Mining user similarity from semantic trajectories
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Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection
Towards location-based social networking services
DC2S: a dynamic car sharing system
A location predictor based on dependencies between multiple lifelog data
Querying geo-social data by bridging spatial networks and social networks
Spatio-temporal proximity and social distance: a confirmation framework for social reporting
Mining user similarity from semantic trajectories
Using location based social networks for quality-aware participatory data transfer
Anonymizing user location and profile information for privacy-aware mobile services
Activity identification from GPS trajectories using spatial temporal POIs' attractiveness

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Mining user similarity from semantic trajectories

Content Provider ACM Digital Library
Author Lu, Eric Hsueh-Chan Lee, Wang-Chien Weng, Tz-Chiao Tseng, Vincent S. Ying, Josh Jia-Ching
Abstract In recent years, research on measuring trajectory similarity has attracted a lot of attentions. Most of similarities are defined based on the geographic features of mobile users' trajectories. However, trajectories geographically close may not necessarily be similar because the activities implied by nearby landmarks they pass through may be different. In this paper, we argue that a better similarity measurement should have taken into account the semantics of trajectories. In this paper, we propose a novel approach for recommending potential friends based on users' semantic trajectories for location-based social networks. The core of our proposal is a novel trajectory similarity measurement, namely, Maximal Semantic Trajectory Pattern Similarity (MSTP-Similarity), which measures the semantic similarity between trajectories. Accordingly, we propose a user similarity measurement based on MSTP-Similarity of user trajectories and use it as the basis for recommending potential friends to a user. Through experimental evaluation, the proposed friend recommendation approach is shown to deliver excellent performance.
Starting Page 19
Ending Page 26
Page Count 8
File Format PDF
ISBN 9781450304344
DOI 10.1145/1867699.1867703
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2010-11-02
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Trajectory database Global positioning system Semantic
Content Type Text
Resource Type Article
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