发表状态 | 已发表Published |
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
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ISSN/eISSN | 2157-6904 |
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 |
DOI | 10.1145/3616541 |
URL | 查看来源 |
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 | |
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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