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    Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties

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    Type
    Conference Paper
    Authors
    Alharbi, Basma Mohammed cc
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2017-02-07
    Online Publication Date
    2017-02-07
    Print Publication Date
    2016-12
    Permanent link to this record
    http://hdl.handle.net/10754/623861
    
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    Abstract
    Location-Based Social Networks (LBSNs) capture individuals whereabouts for a large portion of the population. To utilize this data for user (location)-similarity based tasks, one must map the raw data into a low-dimensional uniform feature space. However, due to the nature of LBSNs, many users have sparse and incomplete check-ins. In this work, we propose to overcome this issue by leveraging the network of friends, when learning the new feature space. We first analyze the impact of friends on individuals's mobility, and show that individuals trajectories are correlated with thoseof their friends and friends of friends (2-hop friends) in an online setting. Based on our observation, we propose a mixed-membership model that infers global mobility patterns from users' check-ins and their network of friends, without impairing the model's complexity. Our proposed model infers global patterns and learns new representations for both usersand locations simultaneously. We evaluate the inferred patterns and compare the quality of the new user representation against baseline methods on a social link prediction problem.
    Citation
    Alharbi B, Zhang X (2016) Learning from Your Network of Friends: A Trajectory Representation Learning Model Based on Online Social Ties. 2016 IEEE 16th International Conference on Data Mining (ICDM). Available: http://dx.doi.org/10.1109/icdm.2016.0090.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2016 IEEE 16th International Conference on Data Mining (ICDM)
    DOI
    10.1109/icdm.2016.0090
    Additional Links
    http://ieeexplore.ieee.org/document/7837903/
    ae974a485f413a2113503eed53cd6c53
    10.1109/icdm.2016.0090
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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