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题名Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment!
作者
发表日期2023-09-30
发表期刊ACM Transactions on Intelligent Systems and Technology
ISSN/eISSN2157-6904
卷号14期号:5
摘要

As a fundamental problem in human mobility modeling, location prediction forecasts a user's next location based on historical user mobility trajectories. Recurrent neural networks (RNNs) have been widely used to capture sequential patterns of user visited locations for solving location prediction problems. Due to the sparse nature of real-world user mobility trajectories, existing techniques strive to improve RNNs by incorporating spatiotemporal contexts into the recurrent hidden state passing process of RNNs using context-parameterized transition matrices or gates. However, such a scheme mismatches universal spatiotemporal mobility laws and thus cannot fully benefit from rich spatiotemporal contexts encoded in user mobility trajectories. Against this background, we propose Flashback++, a general RNN architecture designed for modeling sparse user mobility trajectories. It not only leverages rich spatiotemporal contexts to search past hidden states with high predictive power but also learns to optimally combine them via a hidden state re-weighting mechanism, which significantly improves the robustness of the models against different settings and datasets. Our extensive evaluation compares Flashback++ against a sizable collection of state-of-the-art techniques on two real-world location-based social networks datasets and one on-campus mobility dataset. Results show that Flashback++ not only consistently and significantly outperforms all baseline techniques by 20.56% to 44.36% but also achieves better robustness of location prediction performance against different model settings (different RNN architectures and numbers of hidden states to flash back), different levels of trajectory sparsity, and different train-testing splitting ratios than baselines, yielding an improvement of 31.05% to 94.60%.

关键词Location prediction recurrent neural networks sparse trajectory user mobility
DOI10.1145/3616541
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收录类别SCIE
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号WOS:001087277500014
Scopus入藏号2-s2.0-85174947233
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/10918
专题理工科技学院
通讯作者Yang, Dingqi
作者单位
1.University of Macau, Avenue of University, Macao
2.BNU-HKBU United International College, China
3.Bern University of Applied Sciences, Switzerland
4.University of Fribourg, Switzerland
推荐引用方式
GB/T 7714
Deng, Bangchao,Yang, Dingqi,Qu, Bingqinget al. Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment![J]. ACM Transactions on Intelligent Systems and Technology, 2023, 14(5).
APA Deng, Bangchao, Yang, Dingqi, Qu, Bingqing, Fankhauser, Benjamin, & Cudre-Mauroux, Philippe. (2023). Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment!. ACM Transactions on Intelligent Systems and Technology, 14(5).
MLA Deng, Bangchao,et al."Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment!". ACM Transactions on Intelligent Systems and Technology 14.5(2023).
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