Regularity and conformity: Location prediction using heterogeneous mobility data

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 2015-August, Issue , 2015, Pages 1275-1284

  Wang, Yingzi ^a,b   Yuan, Nicholas Jing ^b   Lian, Defu ^c   Xu, Linli ^a   Xie, Xing ^b   Chen, Enhong ^a   Rui, Yong ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b MICROSOFT RESEARCH   (United States)

^c UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Collaborative filtering; Conformity; Gravity model; Location prediction; Location profile; Regularity; Spatial influence

Indexed keywords

COLLABORATIVE FILTERING; DATA MINING; FORECASTING;

CONFORMITY; GRAVITY MODEL; LOCATION PREDICTION; REGULARITY; SPATIAL INFLUENCES;

LOCATION;

EID: 84954154604     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2783258.2783350     Document Type: Conference Paper

References (41)

1. D. Ashbrook and T. Starner. Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5):275-286, 2003.
2. J. P. Attanucci and N. H. Wilson. Bus passenger origin-destination estimation and related analyses using automated data collection systems. Journal of Public Transportation, 14(4):131, 2011.
3. D. Balcan, V. Colizza, B. Gonçalves, H. Hu, J. J. Ramasco, and A. Vespignani. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences, 106(51):21484-21489, 2009.
4. J. Chang and E. Sun. Location 3: How users share and respond to location-based data on social networking sites. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pages 74-80, 2011.
5. C. Cheng, H. Yang, I. King, and M. R. Lyu. Fused matrix factorization with geographical and social influence in location-based social networks. In AAAI, Volume 12, page 1, 2012.
6. E. Cho, S. A. Myers, and J. Leskovec. 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, pages 1082-1090. ACM, 2011.
7. R. B. Cialdini and N. J. Goldstein. Social influence: Compliance and conformity. Annual review of psychology, 55:591-621, 2004.
8. Y.-A. de Montjoye, C. A. Hidalgo, M. Verleysen, and V. D. Blondel. Unique in the crowd: The privacy bounds of human mobility. Scientific reports, 3, 2013.
9. N. Eagle and A. S. Pentland. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology, 63(7):1057-1066, 2009.
10. H. Gao, J. Tang, and H. Liu. Mobile location prediction in spatio-temporal context. In Nokia mobile data challenge workshop. Citeseer, 2012.
11. H. Gao, J. Tang, and H. Liu. gSCorr: modeling geo-social correlations for new check-ins on location-based social networks. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 1582-1586. ACM, 2012.
12. H. Gao, J. Tang, and H. Liu. Exploring social-historical ties on location-based social networks. In ICWSM, 2012.
13. M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding individual human mobility patterns. Nature, 453(7196):779-782, 2008.
14. H. H. Kelejian and I. R. Prucha. Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics, 157(1):53-67, 2010.
15. M. Kim and D. Kotz. Periodic properties of user mobility and access-point popularity. Personal and Ubiquitous Computing, 11(6): 465-479, 2007.
16. Y.-J. Kim and S.-B. Cho. A HMM-based location prediction framework with location recognizer combining k-nearest neighbor and multiple decision trees. In Hybrid Artificial Intelligent Systems, pages 618-628. Springer, 2013.
17. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30-37, 2009.
18. Z. Li, B. Ding, J. Han, R. Kays, and P. Nye. Mining periodic behaviors for moving objects. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1099-1108. ACM, 2010.
19. D. Lian, X. Xie, V. W. Zheng, N. J. Yuan, F. Zhang, and E. Chen. CEPR: A collaborative exploration and periodically returning model for location prediction. ACM Transactions on Intelligent Systems and Technology (TIST), 6(1), 2014.
20. D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, and Y. Rui. Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 831-840. ACM, 2014.
21. B. Liu, Y. Fu, Z. Yao, and H. Xiong. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1043-1051. ACM, 2013.
22. Y. Liu, W. Wei, A. Sun, and C. Miao. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 739-748. ACM, 2014.
23. A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. Wherenext: a location predictor on trajectory pattern mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 637-646. ACM, 2009.
24. A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. An empirical study of geographic user activity patterns in foursquare. ICWSM, 11: 70-573, 2011.
25. A. Noulas, S. Scellato, N. Lathia, and C. Mascolo. Mining user mobility features for next place prediction in location-based services. In ICDM, pages 1038-1043. Citeseer, 2012.
26. A. Sadilek, H. Kautz, and J. P. Bigham. Finding your friends and following them to where you are. In Proceedings of the fifth ACM international conference on Web search and data mining, pages 723-732. ACM, 2012.
27. S. Scellato, M. Musolesi, C. Mascolo, V. Latora, and A. T. Campbell. Nextplace: a spatio-temporal prediction framework for pervasive systems. In Pervasive Computing, pages 152-169. Springer, 2011.
28. S. Scellato, A. Noulas, R. Lambiotte, and C. Mascolo. Socio-spatial properties of online location-based social networks. ICWSM, 11: 329-336, 2011.
29. M. N. Schmidt, O. Winther, and L. K. Hansen. Bayesian non-negative matrix factorization. In Independent Component Analysis and Signal Separation, pages 540-547. Springer, 2009.
30. J. Scott, A. Bernheim Brush, J. Krumm, B. Meyers, M. Hazas, S. Hodges, and N. Villar. Preheat: controlling home heating using occupancy prediction. In Proceedings of the 13th international conference on Ubiquitous computing, pages 281-290. ACM, 2011.
31. N. Simon, J. Friedman, T. Hastie, and R. Tibshirani. A sparse-group lasso. Journal of Computational and Graphical Statistics, 22(2): 231-245, 2013.
32. C. Song, T. Koren, P. Wang, and A.-L. Barabási. Modelling the scaling properties of human mobility. Nature Physics, 6(10): 818-823, 2010.
33. C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability in human mobility. Science, 327(5968):1018-1021, 2010.
34. L. Song, D. Kotz, R. Jain, and X. He. Evaluating location predictors with extensive wi-fi mobility data. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, Volume 2, pages 1414-1424. IEEE, 2004.
35. D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabasi. Human mobility, social ties, and link prediction. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1100-1108. ACM, 2011.
36. A. G. Wilson. A statistical theory of spatial distribution models. Transportation research, 1(3):253-269, 1967.
37. J. Ye, Z. Zhu, and H. Cheng. WhatâAZs your next move: User activity prediction in location-based social networks. In Proc. of SIAM International Conference on Data Mining (SDM), 2013.
38. N. J. Yuan, Y. Zheng, X. Xie, Y. Wang, K. Zheng, and H. Xiong. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge & Data Engineering, 27(3): 712-725.
39. N. J. Yuan, Y. Wang, F. Zhang, X. Xie, and G. Sun. Reconstructing individual mobility from smart card transactions: A space alignment approach. In Data Mining (ICDM), 2013 IEEE 13th International Conference on, pages 877-886. IEEE, 2013.
40. Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Who, where, when and what: discover spatio-temporal topics for twitter users. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 605-613. ACM, 2013.
41. Y. Zheng, Q. Li, Y. Chen, X. Xie, and W.-Y. Ma. Understanding mobility based on gps data. In Proceedings of the 10th international conference on Ubiquitous computing, pages 312-321. ACM, 2008.