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- Zipei Fan
- SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China, University of Tokyo, Center for Spatial Information Science, Kashiwa, Chiba, Japan
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- Xuan Song
- SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China, University of Tokyo, Center for Spatial Information Science, Kashiwa, Chiba, Japan
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- Renhe Jiang
- University of Tokyo, Center for Spatial Information Science, Kashiwa, Japan
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- Quanjun Chen
- University of Tokyo, Center for Spatial Information Science, Kashiwa, Japan
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- Ryosuke Shibasaki
- University of Tokyo, Center for Spatial Information Science, Kashiwa, Japan
説明
<jats:p>Human mobility prediction is essential to a variety of human-centered computing applications achieved through upgrading of location-based services (LBS) to future-location-based services (FLBS). Previous studies on human mobility prediction have mainly focused on centralized human mobility prediction, where user mobility data are collected, trained and predicted at the cloud server side. However, such a centralized approach leads to a high risk of privacy issues, and a real-time centralized system for processing such a large volume of distributed data is extremely difficult to apply. Moreover, a large and dynamic set of users makes the predictive model extremely challenging to personalize. In this paper, we propose a novel decentralized attention-based human mobility predictor in which 1) no additional training procedure is required for personalized prediction, 2) no additional training procedure is required for incremental learning, and 3) the predictor can be trained and predicted in a decentralized way. We tested our method on big data of real-world mobile phone user GPS and on Android devices, and achieved a low-power consumption and a good prediction accuracy without collecting user data in the server or applying additional training on the user side.</jats:p>
収録刊行物
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- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3 (4), 1-26, 2019-12-11
Association for Computing Machinery (ACM)
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詳細情報 詳細情報について
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- CRID
- 1360849946233838976
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- DOI
- 10.1145/3369830
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- ISSN
- 24749567
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- データソース種別
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- Crossref
- KAKEN