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    Representation Learning with Multi-level Attention for Activity Trajectory Similarity Computation

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    TrajRL.pdf
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    7.502Mb
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    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Liu, An
    Zhang, Yifan
    Zhang, Xiangliang cc
    Liu, Guanfeng
    Zhang, Yanan
    Li, Zhixu
    Zhao, Lei
    Li, Qing
    Zhou, Xiaofang
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2020
    Submitted Date
    2019-09-08
    Permanent link to this record
    http://hdl.handle.net/10754/664294
    
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    Abstract
    Massive trajectory data stem from the prevalence of equipment-supporting GPS and wireless communication technology. Especially, activity trajectory from LBSN endows traditional trajectory data with additional user semantic activities. Measuring the similarity between activity trajectories is to compare their proximity in multiple dimensions such as time, location, and semantics. In this way, we can mine implicit user preference and apply it to route planning, POI recommendation or any other online tasks. The key challenge of comparing activity trajectories lies in two aspects. One is the uneven sampling rate in both time and space. The other is the discrepancy of individual activities. Previous effort alleviates the issue of uneven sampling rate via trajectory complements, which is limited to spatial-temporal information. In this paper, we propose to learn a representation for one activity trajectory by jointly considering the spatio-temporal characteristics and the activity semantics. The similarity of two trajectories is computed by weighting individual trajectory points and contextual features with multi-level attention mechanisms. In specific, we propose a point-level and feature-level attention mechanism to adaptively select critical elements and contextual factors for learning trajectory representation. Our proposed approach, called At2vec, demonstrates better performance than existing baselines in extensive experimental evaluation on real databases.
    Citation
    Liu, A., Zhang, Y., Zhang, X., Liu, G., Zhang, Y., Li, Z., … Zhou, X. (2020). Representation Learning with Multi-level Attention for Activity Trajectory Similarity Computation. IEEE Transactions on Knowledge and Data Engineering, 1–1. doi:10.1109/tkde.2020.3010022
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Knowledge and Data Engineering
    DOI
    10.1109/TKDE.2020.3010022
    Additional Links
    https://ieeexplore.ieee.org/document/9143478/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9143478
    ae974a485f413a2113503eed53cd6c53
    10.1109/TKDE.2020.3010022
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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