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• W3001357734 abstract "Understanding human mobility is one of the important but challenging tasks in Location-based Social Networks (LBSN). Recently, a user mobility mining task called Trajectory User Linking (TUL) has become an essential and popular topic, aiming at identifying user identities through exploiting their mobility patterns. Existing methods mainly focus on learning sequential mobility patterns by capturing long-short term dependencies among historical check-ins. However, users have personalized moving preferences, which have not been considered in previous work. Besides, how to leverage the prior knowledge behind human mobility needs to be further investigated. In this work, we present a novel semi-supervised method, called AdattTUL, to make adversarial mobility learning for human trajectory classification, which is an end-to-end framework modeling human moving patterns. AdattTUL integrates multiple human preferences of check-in behaviors and involves an attention mechanism to dynamically capture the complex relationships of user check-ins from trajectory data. In addition, AdattTUL leverages an adversarial network to help in regularizing the data distribution of human trajectories. Extensive experiments conducted on real-world LBSN datasets show that AdattTUL significantly improves the TUL performance." @default.
• W3001357734 created "2020-01-30" @default.
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• W3001357734 date "2020-01-01" @default.
• W3001357734 modified "2023-10-13" @default.
• W3001357734 title "Adversarial Mobility Learning for Human Trajectory Classification" @default.
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• W3001357734 doi "https://doi.org/10.1109/access.2020.2968935" @default.
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