Abstract
Based on the growing popularity of smart mobile devices, location-aware services become indispensable in human daily life. Location prediction makes these services more intelligent and attractive. However, due to the limited energy of mobile devices and privacy issues, the captured mobility data is typically sparse. This inherent challenge deteriorates significant principles in mobility modeling, i.e. temporal regularity and sequential dependency. To tackle these challenges, by utilizing temporal regularity and sequential dependency, we present a location prediction model with a two-stage fashion. Firstly, it extracts predictive features to effectively target the better performer from sequential and temporal models. Secondly, according to the inferred activity, it adopts non-parametric Kernel Density Estimation for posterior location prediction. Extensive experiments on two public check-in datasets demonstrate that the proposed model outperforms state-of-the-art baselines by 10.1% for activity prediction and 12.9% for location prediction.
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References
Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. IJCAI 13, 2605–2611 (2013)
Cho, E., Myers, S.A., Leskovec, J.: 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, pp. 1082–1090. ACM (2011)
Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new POI recommendation. In: Proceedings of IJCAI (2015)
Kim, S., Park, H.: Fast nonnegative tensor factorization with an active-set-like method. High-Performance Scientific Computing. Springer, Heidalberg (2012). doi:10.1007/978-1-4471-2437-5_16
Li, X., Lian, D., Xie, X., Sun, G.: Lifting the predictability of human mobility on activity trajectories. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1063–1069. IEEE (2015)
Lian, D., Xie, X., Zheng, V.W., Yuan, N.J., Zhang, F., Chen, E.: CEPR: a collaborative exploration and periodically returning model for location prediction. ACM Trans. Intell. Syst. Technol. (TIST) 6(1), 8 (2015)
Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Liu, X., Liu, Y., Aberer, K., Miao, C.: Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 733–738. ACM (2013)
Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: Mining user mobility features for next place prediction in location-based services. In: 2012 IEEE 12th International Conference on Data Mining, pp. 1038–1043. IEEE (2012)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis, vol. 26. CRC Press, Boca Raton (1986)
Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)
Song, L., Kotz, D., Jain, R., He, X.: Evaluating location predictors with extensive wi-fi mobility data. In: INFOCOM 2004, Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 1414–1424. IEEE (2004)
Wang, Y., Yuan, N.J., Lian, D., Xu, L., Xie, X., Chen, E., Rui, Y.: Regularity and conformity: location prediction using heterogeneous mobility data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1275–1284. ACM (2015)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2005)
Yang, D., Zhang, D., Zheng, V.W., Yu, Z.: Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans. Syst. Man Cybern.: Syst. 45(1), 129–142 (2015)
Ye, J., Zhu, Z., Cheng, H.: Whats your next move: user activity prediction in location-based social networks. In: Proceedings of the SIAM International Conference on Data Mining. SIAM (2013)
Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: 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, pp. 605–613. ACM (2013)
Zhang, J.D., Chow, C.Y.: Spatiotemporal sequential influence modeling for location recommendations: a gravity-based approach. ACM Trans. Intell. Syst. Technol. (TIST) 7(1), 11 (2015)
Zhong, Y., Yuan, N.J., Zhong, W., Zhang, F., Xie, X.: You are where you go: inferring demographic attributes from location check-ins. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 295–304. ACM (2015)
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The research is funded by National Key Research and Development Program, investigation on global optimization of resource scheduling method based on the specific application under the contract number No. 2016YFB0201402.
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Liao, D., Zhong, Y., Li, J. (2017). Location Prediction Through Activity Purpose: Integrating Temporal and Sequential Models. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_55
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DOI: https://doi.org/10.1007/978-3-319-57454-7_55
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