Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks
文献类型：期刊论文
| 作者 | Feng, Jie1; Li, Yong1; Yang, Zeyu1; Qiu, Qiang2; Jin, Depeng1 |
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2022-05-01 |
| 卷号 | 34期号:5页码:2360-2374 |
| 关键词 | Trajectory Semantics Predictive models Task analysis Recurrent neural networks Adaptation models Context modeling Neural network attention human mobility |
| ISSN号 | 1041-4347 |
| DOI | 10.1109/TKDE.2020.3006048 |
| 英文摘要 | Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of four challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; 3) the heterogeneity and sparsity of the collected trajectory data; and 4) the complicated semantic motivation behind the mobility. In this paper, we propose DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern human mobility. Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural network for mobility prediction. Furthermore, we design a context adaptor to capture the semantic effects of Point-Of-Interest (POI)-based activity and temporal factor (e.g., dwell time). Finally, we use the multi-task framework to encourage the model to learn comprehensive motivations with mobility by introducing the task of the next activity type prediction and the next check-in time prediction. We perform experiments on four representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10 percent. Moreover, compared with the state-of-the-art neural network models, DeepMove provides intuitive explanations into the prediction and sheds light on interpretable mobility prediction. |
| 资助项目 | National Key Research and Development Program of China[2018YFB1800804] ; National Nature Science Foundation of China[U1936217] ; National Nature Science Foundation of China[61971267] ; National Nature Science Foundation of China[61972223] ; National Nature Science Foundation of China[61941117] ; National Nature Science Foundation of China[61861136003] ; Beijing Natural Science Foundation[L182038] ; Beijing National Research Center for Information Science and Technology[20031887521] ; Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000777332000026 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18912]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Yong |
| 作者单位 | 1.Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol BNRis, Dept Elect Engn, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100084, Peoples R China |
| 推荐引用方式 GB/T 7714 | Feng, Jie,Li, Yong,Yang, Zeyu,et al. Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2022,34(5):2360-2374. |
| APA | Feng, Jie,Li, Yong,Yang, Zeyu,Qiu, Qiang,&Jin, Depeng.(2022).Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,34(5),2360-2374. |
| MLA | Feng, Jie,et al."Predicting Human Mobility With Semantic Motivation via Multi-Task Attentional Recurrent Networks".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 34.5(2022):2360-2374. |
入库方式： OAI收割
来源：计算技术研究所
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