Representation Learning With Multi-Level Attention for Activity Trajectory Similarity Computation

Representation Learning With Multi-Level Attention for Activity Trajectory Similarity Computation

Massive trajectory data stem from the prevalence of equipment-supporting GPS and wireless communication technology. Especially, activity trajectory from Location-based Social Network (LBSN) endows traditional trajectory data with additional user semantic activities, e.g., visiting work/home/entertai...

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Bibliographic Details
Main Authors: Li, Q. (Author), Li, Z. (Author), Liu, A. (Author), Liu, G. (Author), Zhang, X. (Author), Zhang, Y. (Author), Zhao, L. (Author), Zhou, X. (Author)
Format: Article
Language:English
Published: IEEE Computer Society 2022
Subjects:
activity trajectory
Activity trajectory
attention mechanisms
Attention mechanisms
Location-based social networks
Multilevels
representation learning
Representation learning
Sampling rates
Semantics
Similarity computation
Social sciences computing
Trajectories
Trajectories datum
Trajectory similarities
Trajectory similarity
Wireless communication technology
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