Abstract
The huge amount of location tracker data generated by electronic devices makes them an ideal source of information for detecting trends and behaviors in their users’ lives. Learning these patterns is very important for recommender systems or applications targeted at behavior prediction. In this work we show how user location history can be processed in order to extract the most relevant visited locations and to model the user’s profile through a weighted finite automaton, a probabilistic graphical structure that is able to handle locations and temporal context compactly. Our condensed representation gives access to the user’s routines and can play an important role in recommender systems.
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References
Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 330–339. ACM, New York (2007)
Hildebrandt, M.: Defining profiling: a new type of knowledge? In: Hildebrandt, M., Gutwirth, S. (eds.) Profiling the European Citizen. Springer, Dordrecht (2008). doi:10.1007/978-1-4020-6914-7_2
Hung, C.C., Chang, C.W., Peng, W.C.: Mining trajectory profiles for discovering user communities. In: Proceedings of the 2009 International Workshop on Location Based Social Networks, LBSN 2009, pp. 1–8. ACM, New York (2009)
Kahle, D., Wickham, H.: ggmap: spatial visualization with ggplot2. R J. 5(1), 144–161 (2013)
Karli, S., Saygin, Y.: Mining periodic patterns in spatio-temporal sequences at different time granularities. Intell. Data Anal. 13(2), 301–335 (2009)
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2008, pp. 34: 1–34: 10. ACM, New York (2008)
Tîrnăucă, C., Duque, R., Montaña, J.L.: Automatic generation of user interaction models. In: García, C.R., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds.) UCAmI 2016. LNCS, vol. 10069, pp. 406–418. Springer, Cham (2016). doi:10.1007/978-3-319-48746-5_42
Weiser, M.: The computer for the 21st century. In: Baecker, R.M., Grudin, J., Buxton, W.A.S., Greenberg, S. (eds.) Human-computer Interaction: toward the year 2000, pp. 933–940. Morgan Kaufmann Publishers Inc., San Francisco (1995)
Ye, Y., Zheng, Y., Chen, Y., Feng, J., Xie, X.: Mining individual life pattern based on location history. In: Proceedings - IEEE International Conference on Mobile Data Management, pp. 1–10 (2009)
Yuan, J., Zheng, Y., Zhang, L., Xie, X., Sun, G.: Where to find my next passenger. In: Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp 2011, pp. 109–118. ACM, New York (2011)
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6(3), 29: 1–29: 41 (2015)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.Y.: Understanding mobility based on gps data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp 2008, pp. 312–321. ACM, New York (2008)
Acknowledgments
The authors gratefully acknowledge the financial support from the project TI16-IN-007 of SODERCAN and project PAC::LFO (MTM2014-55262-P) of Ministerio de Ciencia e Innovación (MICINN), Spain. The geographic maps included in this paper were plotted using the ggmap R package [4].
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Salomón, S., Tîrnăucă, C., Duque, R., Montaña, J.L. (2017). Daily Routines Inference Based on Location History. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_80
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DOI: https://doi.org/10.1007/978-3-319-67585-5_80
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