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Predicting the next place to visit is a key in human mobility behavior modeling, which plays a significant role in various fields, such as epidemic control, urban planning, traffic management, and travel recommendation.
M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding individual human mobility patterns,” nature , vol. 453, no. 7196, pp. 779–782, 2008
2008
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C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, “Limits of predictability in human mobility,” Science , vol. 327, no. 5968, pp. 1018–1021, 2010
2010
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S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized markov chains for next-basket recommendation,” in Proceedings of the 19th international conference on world wide web , 2010, pp. 811–820
2010
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C. M. Schneider, V. Belik, T. Couronné, Z. Smoreda, and M. C. González, “Unravelling daily human mobility motifs,” Journal of The Royal Society Interface , vol. 10, no. 84, p. 20130246, 2013
2013
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C. Cheng, H. Yang, M. R. Lyu, and I. King, “Where you like to go next: Successive point-of-interest recommendation,” in Twenty-Third international joint conference on Artificial Intelligence , 2013
2013
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M. Tizzoni, P. Bajardi, A. Decuyper, G. K. K. King, C. M. Schneider, V. Blondel, Z. Smoreda, M. C. González, and V. Colizza, “On the use of human mobility proxies for modeling epidemics,” PLoS Comput Biol , vol. 10, no. 7, p. e1003716, 2014
2014
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2015
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S. Jiang, Y. Yang, S. Gupta, D. Veneziano, S. Athavale, and M. C. González, “The timegeo modeling framework for urban mobility without travel surveys,” Proceedings of the National Academy of Sciences , vol. 113, no. 37, pp. E5370–E5378, 2016
2016
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S. Çolak, A. Lima, and M. C. González, “Understanding congested travel in urban areas,” Nature Communications , vol. 7, no. 1, pp. 1–8, 2016
2016
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Q. Liu, S. Wu, L. Wang, and T. Tan, “Predicting the next location: A recurrent model with spatial and temporal contexts,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 30, no. 1, 2016
2016
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A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining , 2016, pp. 855–864
2016
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Y. Xu and M. C. González, “Collective benefits in traffic during mega events via the use of information technologies,” Journal of The Royal Society Interface , vol. 14, no. 129, p. 20161041, 2017
2017
Earlier work this paper cites.
F. Alhasoun, M. Alhazzani, F. Aleissa, R. Alnasser, and M. González, “City scale next place prediction from sparse data through similar strangers,” in Proceedings of ACM KDD Workshop , 2017, pp. 191–196
2017
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J. Manotumruksa, C. Macdonald, and I. Ounis, “A deep recurrent collaborative filtering framework for venue recommendation,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , 2017, pp. 1429–1438
2017
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D. Yao, C. Zhang, J. Huang, and J. Bi, “Serm: A recurrent model for next location prediction in semantic trajectories,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , 2017, pp. 2411–2414
2017
Cited alongside, same era.
L. E. Olmos, S. Çolak, S. Shafiei, M. Saberi, and M. C. González, “Macroscopic dynamics and the collapse of urban traffic,” Proceedings of the National Academy of Sciences , vol. 115, no. 50, pp. 12 654–12 661, 2018
2018
Cited alongside, same era.
Y. Xu, S. Çolak, E. C. Kara, S. J. Moura, and M. C. González, “Planning for electric vehicle needs by coupling charging profiles with urban mobility,” Nature Energy , vol. 3, pp. 484–493, 2018
2018
Cited alongside, same era.
J. Feng, Y. Li, C. Zhang, F. Sun, F. Meng, A. Guo, and D. Jin, “Deepmove: Predicting human mobility with attentional recurrent networks,” in Proceedings of the 2018 world wide web conference , 2018, pp. 1459–1468
2018
Cited alongside, same era.
2019
Later among the works it cites.
Z. Zhang, C. Li, Z. Wu, A. Sun, D. Ye, and X. Luo, “Next: a neural network framework for next poi recommendation,” Frontiers of Computer Science , vol. 14, no. 2, pp. 314–333, 2020
2020
Later among the works it cites.
S. Chang, E. Pierson, P. W. Koh, J. Gerardin, B. Redbird, D. Grusky, and J. Leskovec, “Mobility network models of covid-19 explain inequities and inform reopening,” Nature , pp. 1–8, 2020
2020
Later among the works it cites.
J. Jiang, S. Tao, D. Lian, Z. Huang, and E. Chen, “Predicting human mobility with self-attention and feature interaction,” in Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data . Springer, 2020, pp. 117–131
2020
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L. Alessandretti, P. Sapiezynski, V. Sekara, S. Lehmann, and A. Baronchelli, “Evidence for a conserved quantity in human mobility,” Nature human behaviour , vol. 2, no. 7, pp. 485–491, 2018
2018
Cited alongside, same era.
Y. Xu, S. Jiang, R. Li, J. Zhang, J. Zhao, S. Abbar, and M. C. González, “Unraveling environmental justice in ambient PM 2.5
2019
Cited alongside, same era.
Y. Wu, K. Li, G. Zhao, and X. Qian, “Long-and short-term preference learning for next poi recommendation,” in Proceedings of the 28th ACM international conference on information and knowledge management , 2019, pp. 2301–2304
2019
Cited alongside, same era.
D. Lian, Y. Wu, Y. Ge, X. Xie, and E. Chen, “Geography-aware sequential location recommendation,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 2009–2019
2019
Cited alongside, same era.
W. Lan, Y. Xu, and B. Zhao, “Travel time estimation without road networks: an urban morphological layout representation approach,” in Twenty-Eighth International Joint Conferences on Artifical Intelligence (IJCAI) , 2019, pp. 1772–1778
2019
Cited alongside, same era.
Q. Gao, F. Zhou, G. Trajcevski, K. Zhang, T. Zhong, and F. Zhang, “Predicting human mobility via variational attention,” in The World Wide Web Conference , 2019, pp. 2750–2756
2019
Cited alongside, same era.
A. Khazane, J. Rider, M. Serpe, A. Gogoglou, K. Hines, C. B. Bruss, and R. Serpe, “Deeptrax: Embedding graphs of financial transactions,” in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) . IEEE, 2019, pp. 126–133
2019
Cited alongside, same era.
K. Ding, J. Li, R. Bhanushali, and H. Liu, “Deep anomaly detection on attributed networks,” in Proceedings of the 2019 SIAM International Conference on Data Mining . SIAM, 2019, pp. 594–602
2019
Cited alongside, same era.
Later among the works it cites.
J. Chen, J. Li, and Y. Li, “Predicting human mobility via long short-term patterns,” Computer Modeling in Engineering & Sciences , vol. 124, no. 3, pp. 847–864, 2020
2020
Later among the works it cites.
K. Sun, T. Qian, T. Chen, Y. Liang, Q. V. H. Nguyen, and H. Yin, “Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 01, 2020, pp. 214–221
2020
Later among the works it cites.
Q. Guo, Z. Sun, J. Zhang, and Y.-L. Theng, “An attentional recurrent neural network for personalized next location recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 01, 2020, pp. 83–90
2020
Later among the works it cites.
W. Liang and W. Zhang, “Learning social relations and spatiotemporal trajectories for next check-in inference,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
Later among the works it cites.
Y. Wu, K. Li, G. Zhao, and Q. Xueming, “Personalized long-and short-term preference learning for next poi recommendation,” IEEE Transactions on Knowledge and Data Engineering , 2020
2020
Later among the works it cites.
P. Zhao, A. Luo, Y. Liu, F. Zhuang, J. Xu, Z. Li, V. S. Sheng, and X. Zhou, “Where to go next: A spatio-temporal gated network for next poi recommendation,” IEEE Transactions on Knowledge and Data Engineering , 2020
2020
Later among the works it cites.
J. Chen, J. Li, M. Ahmed, J. Pang, M. Lu, and X. Sun, “Next location prediction with a graph convolutional network based on a seq2seq framework,” KSII Transactions on Internet and Information Systems (TIIS) , vol. 14, no. 5, pp. 1909–1928, 2020
2020
Later among the works it cites.
Z. Lu, P. Du, and J.-Y. Nie, “Vgcn-bert: augmenting bert with graph embedding for text classification,” in European Conference on Information Retrieval . Springer, 2020, pp. 369–382
2020
Later among the works it cites.
Y. Xu, R. Di Clemente, and M. C. González, “Understanding vehicular routing behavior with location-based service data,” EPJ Data Science , vol. 10, no. 1, pp. 1–17, 2021
2021
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