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http://dx.doi.org/10.3837/tiis.2020.05.003

Next Location Prediction with a Graph Convolutional Network Based on a Seq2seq Framework  

Chen, Jianwei (Computer Science and Technology, Qingdao University)
Li, Jianbo (Computer Science and Technology, Qingdao University)
Ahmed, Manzoor (Computer Science and Technology, Qingdao University)
Pang, Junjie (Computer Science and Technology, Qingdao University)
Lu, Minchao (Computer Science and Technology, Qingdao University)
Sun, Xiufang (Computer Science and Technology, Qingdao University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.5, 2020 , pp. 1909-1928 More about this Journal
Abstract
Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.
Keywords
Location Prediction; Spatial-Temporal Dependence; Seq2seq Framework; GCN;
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1 S. Lee, J. Lim, J. Park and K. Kim, "Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs," Sensors (Basel), 16(2), 145, 2016.   DOI
2 A. Karatzoglou, S. C. Lamp and M. Beigl, "Matrix factorization on semantic trajectories for predicting future semantic locations," in Proc. of 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 1-7, October 9-11, 2017.
3 C. Cheng, H. Yang, I. King, et al, "Fused matrix factorization with geographical and social influence in location-based social networks," in Proc. of Twenty-Sixth AAAI Conference on Artificial Intelligence, July 22-26, 2012.
4 Z. Montazeri, A. Houmansadr and H. Pishro-Nik, "Achieving Perfect Location Privacy in Wireless Devices Using Anonymization," IEEE Transactions on Information Forensics and Security, vol. 12, no. 11, pp. 2683-2698, 2017.   DOI
5 T. Mikolov, M. Karafiat, L. Burget, J. H. Cernocky, and S. Khudanpur, "Recurrent neural network-based language model," INTERSPEECH, pp. 1045-1048, 2010.
6 B. Wang, W. Kong, H. Guan, et al, "Air Quality Forcasting based on Gated Recurrent Long Short-Term Memory Model in Internet of Things," IEEE Access, pp. 69524-69534, 2019.   DOI
7 M. Wang, S. Niu and Z. Gao, "A Novel Scene Text Recognition Method Based on Deep Learning," Computers, Materials & Continua, Vol. 60, No. 2, pp.781-794, 2019.   DOI
8 A. Zarezade, S. Jafarzadeh, and H. Rabiee, "Recurrent Spatio-temporal modeling of check-ins in location-based social networks," PLOS ONE, vol. 13, 11 2016.
9 I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks," in Proc. of the 27th International Conference on Neural Information Processing Systems, pp. 3104-3112, 2014.
10 Z. Xia, Z. Hu and J. Luo, "UPTP Vehicle Trajectory Prediction Based on User Preference Under Complexity Environment," Wireless Personal Communications, vol. 97 no. 3, pp. 4651-4665, 2017.   DOI
11 C. Feng, S. Arshad, S. Zhou, D. Cao and Y. Liu, "Wi-multi: A Three-phase System for Multiple Human Activity Recognition with Commercial WiFi Devices," IEEE Internet of Things Journal, vol. 6, no. 4, pp. 7293-7304, 2019.   DOI
12 D. Ashbrook and T. Starner, "Learning significant locations and predicting user movement with GPS," in Proc. of Sixth International Symposium on Wearable Computers, pp. 101-108, October 7-10, 2002.
13 G. Yavas, Dimitrios K., Ozgur Ulusoy, Y.Manolopoulos, "A data mining approach for location prediction in mobile environments," Data & Knowledge Engineering, Vol. 54, no. 2, pp. 121-146, 2005.   DOI
14 L. Zhao, Y. Song, M. Deng, and H. Li, "Temporal graph convolutional network for urban traffic flow prediction method," IEEE Transactions on Intelligent Transportation Systems, 2018.
15 A. Noulas, S. Scellato, N. Lathia and C. Mascolo, "Mining User Mobility Features for Next Place Prediction in Location-Based Services," in Proc. of IEEE 12th International Conference on Data Mining, pp. 1038-1043, December 10-13, 2012.
16 C. Song, Z. Qu, N. Blumm and A.-Laszlo Barabasi, "Limits of Predictability in Human Mobility," Science, vol. 327, no. 5968, pp. 1018-1021, 2010.   DOI
17 S. Park, B. Kim, C. Mook Kang, C. Choo Chung, et al, "Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture," in Proc. of IEEE Intelligent Vehicles Symposium (IV), pp. 1672-1678, 2018.
18 D. D. Nguyen, C. Le Van, and M. I. Ali, "Vessel trajectory prediction using sequence-to-sequence models over spatial grid," in Proc. of the 12th ACM International Conference on Distributed and Event-based Systems, pp. 258-261, June 25-29, 2018.
19 J. Feng, Y. Li, C. Zhang, et al, "Deepmove: Predicting human mobility with attentional recurrent networks," in Proc. of the 2018 world wide web conference, pp. 1459-1468, April 23-27, 2018.
20 T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks," arXiv preprint arXiv:1609.02907, 2016.
21 Y. Li, R. Yu, C. Shahabi, and Y. Liu, "Diffusion convolutional recurrent neural network: Data-driven traffic forecasting," arXiv preprint arXiv:1707.01926, 2017.
22 D. K. Hammond, P. Vandergheynst, and R. Gribonval, "Wavelets on graphs via spectral graph theory," Applied and Computational Harmonic Analysis, vol. 30, no. 2, pp. 129-150, 2011.   DOI
23 M. Schlichtkrull, T N. Kipf, P. Bloem, et al, "Modeling relational data with graph convolutional networks," in Proc. of European Semantic Web Conference. pp. 593-607, June 3-7, 2018.
24 C. Li, Y. Jiang and M. Cheslyar, "Embedding Image Through Generated Intermediate Medium Using Deep Convolutional Generative Adversarial Network," Computers, Materials & Continua, Vol.56, No.2, pp.313-324,2018.
25 H. Yin and O. Wolfson, "A weight-based map matching method in moving objects databases," in Proc. of 16th International Conference on Scientific and Statistical Database Management, pp. 437-438, June 23, 2004.
26 C. Cheng, Y. Haiqin and L. Michael & K. Irwin, "Where you like to go next: Successive point-of-interest recommendation," in Proc. of IJCAI International Joint Conference on Artificial Intelligence. pp. 2605-2611, August 3-9, 2013.
27 S. Gambs, M.-O. Killijian, and M. N. n. del Prado Cortez, "Next place prediction using mobility markov chains," in Proc. of the First Workshop on Measurement, Privacy, and Mobility, ACM, pp. 1-6, 2012.
28 A. Al-Molegi, M. Jabreel, and B. Ghaleb, "Stf-rnn: Space time features based recurrent neural network for predicting people next location," in Proc. of 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-7, December 6-9, 2016.
29 H. Wang, Z. Li, and W. Lee, "Pgt: Measuring mobility relationship using personal, global and temporal factors," in Proc. of 2014 IEEE International Conference on Data Mining, pp. 570-579, December 14-17, 2014.
30 F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, "Trajectory pattern mining," in Proc. of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 330-339, 2007.
31 A. Farzad and R. N. Asli, "Recognition and classification of human behavior in intelligent surveillance systems using hidden markov model," IJ Image, Graphics and Signal Processing, pp. 31-38, 2015.
32 T. Kim, S. Taylor, Y. Yue, and I. Matthews, "A decision tree framework for spatiotemporal sequence prediction," in Proc. of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 577-586, 2015.
33 W. Mathew, R. Raposo, and B. Martins, "Predicting future locations with hidden markov models," in Proc. of the 2012 ACM Conference on Ubiquitous Computing, pp. 911-918, September 5-8, 2012.
34 J. Ye, Z. Zhu, and H. Cheng, "What's Your Next Move: User Activity Prediction in Location-based Social Networks," in Proc. of the 2013 SIAM International Conference on Data Mining, pp. 171-179, May 2-4, 2013.
35 A. Gellert and L. Vintan, "Person movement prediction using hidden markov models," Studies in Informatics and Control, vol. 15, pp. 17-30, 2006.
36 Q. Liu, S. Wu, L. Wang, and T. Tan, "Predicting the next location: A recurrent model with spatial and temporal contexts," in Proc. of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 194-200, February 12-17, 2016.
37 D. Yao, C. Zhang, J. Huang, and J. Bi, "Serm: A recurrent model for next location prediction in semantic trajectories," in Proc. of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2411-2414, 2017.
38 N. Du, H. Dai, R. Trivedi, U. Upadhyay, M. Gomez-Rodriguez, and L. Song, "Recurrent marked temporal point processes: Embedding event history to vector," in Proc. of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1555-1564, 2016.
39 A. Asahara, K. Maruyama, A. Sato, and K. Seto, "Pedestrianmovement prediction based on mixed markov-chain model," in Proc. of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 25-33, November 1-4, 2011.
40 Q. Li and H. C. Lau, "A Layered Hidden Markov Model for Predicting Human Trajectories in a Multi-floor Building," in Proc. of 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 344-351, December 6-9, 2015.
41 A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti, "Wherenext: A location predictor on trajectory pattern mining," in Proc. of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 637-646, 2009.
42 E. Cho, S. A. Myers, and J. Leskovec, "Friendship and mobility: user movement in location-based social networks," in Proc. of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 1082-1090, 2011.
43 H. K. Shin, Y. H. Ahn, S. H. Lee and H. Y. Kim, "Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep ConVolutional Neural Network," Computers, Materials & Continua, Vol. 61, No. 3, pp.911-928, 2019.   DOI
44 K. Xu, L. Wu, Z. Wang, Y. Feng, M. Witbrock, and V. Sheinin, "Graph2seq: Graph to sequence learning with attention-based neural networks," arXiv preprint arXiv:1804.00823, 2018.
45 D. Bahdanau, K. Cho and Y. Bengio, "Neural Machine Translation by Jointly Learning to Align and Translate," arXiv preprint arXiv:1409.0473, 2014.
46 D. Yang, D. Zhang, V. W. Zheng, and Z. Yu, "Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 1, pp. 129-142, 2014.   DOI