ABSTRACT
Human mobility prediction is of great importance for a wide spectrum of location-based applications. However, predicting mobility is not trivial because of three challenges: 1) the complex sequential transition regularities exhibited with time-dependent and high-order nature; 2) the multi-level periodicity of human mobility; and 3) the heterogeneity and sparsity of the collected trajectory data. 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 the 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. We perform experiments on three representative real-life mobility datasets, and extensive evaluation results demonstrate that our model outperforms the state-of-the-art models by more than 10%. 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.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural Machine Translation by Jointly Learning to Align and Translate. Computer Science (2014).Google Scholar
- Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17). ACM, New York, NY, USA, 335--344. Google ScholarDigital Library
- Meng Chen, Yang Liu, and Xiaohui Yu. 2014. NLPMM: A Next Location Predictor with Markov Modeling. 8444 (2014), 186--197.Google Scholar
- C. Cheng, H. Yang, I. King, and M. R. Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In AAAI Conference on Artificial Intelligence. Google ScholarDigital Library
- Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. 2013. Where you like to go next: successive point-of-interest recommendation. In International Joint Conference on Artificial Intelligence. Google ScholarDigital Library
- Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and Mobility: User Movement in Location-based Social Networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11). ACM, New York, NY, USA, 1082--1090. Google ScholarDigital Library
- Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. CoRR abs/1412.3555 (2014). http://arxiv.org/abs/1412.3555Google Scholar
- Justin Cranshaw, Eran Toch, Jason Hong, Aniket Kittur, and Norman Sadeh. 2010. Bridging the Gap Between Physical Location and Online Social Networks. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing (UbiComp '10). ACM, New York, NY, USA, 119--128. Google ScholarDigital Library
- Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel GomezRodriguez, and Le Song. 2016. Recurrent Marked Temporal Point Processes: Embedding Event History to Vector. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 1555--1564. Google ScholarDigital Library
- M. C. GonzÃlez, C. A. Hidalgo, and A. L. BarabÃ'si. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779.Google Scholar
- Alex Graves. 2008. Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence 385 (2008).Google Scholar
- Alex Graves. 2013. Generating Sequences With Recurrent Neural Networks. Computer Science (2013).Google Scholar
- BalÃzs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based Recommendations with Recurrent Neural Networks. Computer Science (2015).Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Comput. 9, 8 (Nov. 1997), 1735--1780. Google ScholarDigital Library
- Jintao Ke Xianfeng Tang Yitian Jia Siyu Lu Pinghua Gong Jieping Ye Huaxiu Yao, Fei Wu and Zhenhui Li. 2018. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. In Proceedings of 2018 AAAI Conference on Artificial Intelligence (AAAI '18).Google Scholar
- Marc Olivier Killijian. 2012. Next place prediction using mobility Markov chains. In The Workshop on Measurement, Privacy, and Mobility. 3. Google ScholarDigital Library
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980Google Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (2009), 30--37. Google ScholarDigital Library
- Zachary C Lipton, John Berkowitz, and Charles Elkan. 2015. A Critical Review of Recurrent Neural Networks for Sequence Learning. Computer Science (2015).Google Scholar
- Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: a recurrent model with spatial and temporal contexts. In Thirtieth AAAI Conference on Artificial Intelligence. 194--200. Google ScholarDigital Library
- Minh Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective Approaches to Attention-based Neural Machine Translation. Computer Science (2015).Google Scholar
- Wesley Mathew, Ruben Raposo, and Bruno Martins. 2012. Predicting future locations with hidden Markov models. In ACM Conference on Ubiquitous Computing. 911--918. Google ScholarDigital Library
- Hongyuan Mei and Jason Eisner. 2017. The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process. In Advances in Neural Information Processing Systems. Long Beach. https://arxiv.org/abs/1612.09328Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 26 (2013), 3111--3119. Google ScholarDigital Library
- Anna Monreale, Fabio Pinelli, Roberto Trasarti, and Fosca Giannotti. 2009. WhereNext:a location predictor on trajectory pattern mining. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July. 637--646. Google ScholarDigital Library
- Fabio Pinelli, Fabio Pinelli, Fabio Pinelli, and Dino Pedreschi. 2007. Trajectory pattern mining. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 330--339. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In International Conference on World Wide Web. 811--820. Google ScholarDigital Library
- C. Song, Z. Qu, N Blumm, and A. L. BarabÃ'si. 2010. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018.Google Scholar
- Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Hindawi Publishing Corp. 4 pages.Google Scholar
- Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. 4 (2014), 3104--3112. Google ScholarDigital Library
- Hongjian Wang and Zhenhui Li. 2017. Region Representation Learning via Mobility Flow. In CIKM. Google ScholarDigital Library
- Fei Wu and Zhenhui Li. 2016. Where Did You Go: Personalized Annotation of Mobility Records. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM '16). ACM, New York, NY, USA, 589--598. Google ScholarDigital Library
- Fei Wu, Zhenhui Li, Wang-Chien Lee, Hongjian Wang, and Zhuojie Huang. 2015. Semantic Annotation of Mobility Data Using Social Media. In Proceedings of the 24th International Conference on World Wide Web (WWW '15). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1253--1263. Google ScholarDigital Library
- Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, and Depeng Jin. 2017. Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data. In International Conference on World Wide Web. 1241--1250. Google ScholarDigital Library
- Fengli Xu, Pengyu Zhang, and Yong Li. 2016. Context-aware Real-time Population Estimation for Metropolis. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '16). ACM, New York, NY, USA, 1064--1075. Google ScholarDigital Library
- Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio. 2015. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Computer Science (2015), 2048--2057. Google ScholarDigital Library
- Cheng Yang, Maosong Sun, Wayne Xin Zhao, Zhiyuan Liu, and Edward Y. Chang. 2017. A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories. ACM Trans. Inf. Syst. 35, 4, Article 36 (Aug. 2017), 28 pages. Google ScholarDigital Library
- Di Yao, Chao Zhang, Jianhui Huang, and Jingping bi. 2017. SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories. In ACM International on Conference on Information and Knowledge Management. 2411--2414. Google ScholarDigital Library
- Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek Abdelzaher. 2017. DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 351--360. Google ScholarDigital Library
- Jia Ching Ying, Wang Chien Lee, Tz Chiao Weng, and Vincent S. Tseng. 2011. Semantic trajectory mining for location prediction. In ACM Sigspatial International Conference on Advances in Geographic Information Systems. 34--43. Google ScholarDigital Library
- Quan Yuan, Wei Zhang, Chao Zhang, Xinhe Geng, Gao Cong, and Jiawei Han. 2017. PRED: Periodic Region Detection for Mobility Modeling of Social Media Users. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM '17). ACM, New York, NY, USA, 263--272. Google ScholarDigital Library
- Chao Zhang, Jiawei Han, Lidan Shou, Jiajun Lu, and Thomas La Porta. 2014. Splitter: Mining Fine-grained Sequential Patterns in Semantic Trajectories. Proc. VLDB Endow. 7, 9 (May 2014), 769--780. Google ScholarDigital Library
- C. Zhang, K. Zhang, Q. Yuan, L. Zhang, T Hanratty, and J. Han. 2016. GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1305--1314. Google ScholarDigital Library
- Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, and Tie Yan Liu. 2014. Sequential click prediction for sponsored search with recurrent neural networks. In Twenty-Eighth AAAI Conference on Artificial Intelligence. 1369--1375. Google ScholarDigital Library
Index Terms
- DeepMove: Predicting Human Mobility with Attentional Recurrent Networks
Recommendations
Convolutional Recurrent Neural Networks for Text Classification
Recurrent neural network (RNN) and convolutional neural network (CNN) are two prevailing architectures used in text classification. Traditional approaches combine the strengths of these two networks by straightly streamlining them or linking features ...
Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation
The back-propagation neural network (BPN) model has been the most popular form of artificial neural network model used for forecasting, particularly in economics and finance. It is a static (feed-forward) model which has a learning process in both hidden ...
Exploiting Context and Attention Using Recurrent Neural Network for Sensor Time Series Prediction
Advanced Analytics and Learning on Temporal DataAbstractIn the current era of Internet of Things, typically data from multiple sources are captured through various sensors yielding Multivariate Time Series (MTS) data. Sensor MTS prediction has several real-life applications in various domains such as ...
Comments