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MPE: a mobility pattern embedding model for predicting next locations


The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each record having at least three attributes: object ID, location ID, and time-stamp. In this paper, we propose a novel mobility pattern embedding model called MPE to shed the light on people’s mobility patterns in traffic trajectory data from multiple aspects, including sequential, personal, and temporal factors. MPE has two salient features: (1) it is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space; (2) it considers the effect of “phantom transitions” arising from road networks in traffic trajectory data. This embedding model opens the door to a wide range of applications such as next location prediction and visualization. Experimental results on two real-world datasets show that MPE is effective and outperforms the state-of-the-art methods significantly in a variety of tasks.

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  1. Chen, M., Liu, Y., Yu, X.: Nlpmm: a next location predictor with markov modeling. In: PAKDD, pp. 186–197. Springer (2014)

  2. Chen, M., Yu, X., Liu, Y.: Mining moving patterns for predicting next location. Inf. Syst. 54, 156–168 (2015)

    Article  Google Scholar 

  3. de Brébisson, A., Simon, É., Auvolat, A., Vincent, P., Bengio, Y.: Artificial neural networks applied to taxi destination prediction. arXiv:1508.00021 (2015)

  4. Dong, Z., Yu, X., Cui, X., Song, R., Lin, L.: Grandland traffic data processing platform. JCRD, pp. 766–767 (2014)

  5. Feng, S., Cong, G., An, B., Chee, Y.M.: Poi2vec: geographical latent representation for predicting future visitors. In: AAAI, pp. 102–108 (2017)

  6. Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new poi recommendation. In: IJCAI, pp. 2069–2075. AAAI Press (2015)

  7. Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: ICML, pp. 1764–1772 (2014)

  8. Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: KDD, pp. 855–864. ACM (2016)

  9. Hinton, G., Roweis, S.: Stochastic neighbor embedding. NIPS 41(4), 833–840 (2010)

    Google Scholar 

  10. Jia, Y., Wang, Y., Jin, X., Cheng, X.: Location prediction: a temporal-spatial bayesian model. TIST 7(3), 31 (2016)

    Article  Google Scholar 

  11. Jiang, W., Zhu, J., Xu, J., Li, Z., Zhao, P., Zhao, L.: A feature based method for trajectory dataset segmentation and profiling. WWW 20(1), 5–22 (2017)

    Article  Google Scholar 

  12. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–C1196 (2014)

  13. Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. NIPS 3, 2177–2185 (2014)

    Google Scholar 

  14. Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: KDD, pp. 831–840. ACM (2014)

  15. Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI, pp. 194–200 (2016)

  16. Maaten, L., Hinton, G.: Visualizing data using t-sne. JMLR 9(11), 2579–2605 (2008)

    MATH  Google Scholar 

  17. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. NIPS 26, 3111–3119 (2013)

    Google Scholar 

  18. Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: Wherenext: a location predictor on trajectory pattern mining. In: KDD, pp. 637–646. ACM (2009)

  19. Wang, D., Deng, S., Liu, S., Xu, G.: Improving music recommendation using distributed representation. In: WWW, pp. 125–126. ACM (2016)

  20. Xue, A.Y., Zhang, R., Zheng, Y., Xie, X., Huang, J., Xu, Z.: Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: ICDE, pp. 254–265. IEEE (2013)

  21. Yao, Z., Fu, Y., Liu, B., Liu, Y., Xiong, H.: Poi recommendation: a temporal matching between poi popularity and user regularity. In: ICDM, pp. 549–558. IEEE (2016)

  22. Ye, J., Zhu, Z., Cheng, H.: What’s your next move: user activity prediction in location-based social networks. In: SDM, pp. 171–179. SIAM (2013)

  23. Yin, H., Cui, B., Huang, Z., Wang, W., Wu, X., Zhou, X.: Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In: MM, pp. 819–822. ACM (2015)

  24. Yuan, N.J., Zheng, Y., Xie, X., Wang, Y., Zheng, K., Xiong, H.: Discovering urban functional zones using latent activity trajectories. TKDE 27(3), 712–725 (2015)

    Google Scholar 

  25. Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Who, where, when and what: discover spatio-temporal topics for twitter users. In: KDD, pp. 605–613 (2013)

  26. Yuan, Q., Cong, G., Zhao, K., Ma, Z., Sun, A.: Who, where, when, and what: a nonparametric bayesian approach to context-aware recommendation and search for twitter users. TOIS 33(1), 1–33 (2015)

    Article  Google Scholar 

  27. Zhang, C., Zhang, K., Yuan, Q., Zhang, L., Hanratty, T., Han, J.: Gmove: group-level mobility modeling using geo-tagged social media. In: KDD, pp. 1305–1314. ACM (2016)

  28. Zhang, D., Zhao, S., Yang, L.T., Chen, M., Wang, Y., Liu, H.: Nextme: localization using cellular traces in internet of things. TII 11(2), 302–312 (2015)

    Google Scholar 

  29. Zhao, S., Zhao, T., King, I., Lyu, M.R.: Geo-Teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In: WWW, pp. 153–162 (2017)

  30. Zhao, S., Zhao, T., Yang, H., Lyu, M.R., King, I.: Stellar: spatial-temporal latent ranking for successive point-of-interest recommendation. In: AAAI, pp. 315–322 (2016)

  31. Zheng, K., Zheng, B., Xu, J., Liu, G., Liu, A., Li, Z.: Popularity-aware spatial keyword search on activity trajectories. WWW 20(4), 749–773 (2017)

    Article  Google Scholar 

  32. Zheng, Y.: Trajectory data mining: an overview. TIST 6(3), 29 (2015)

    Article  Google Scholar 

  33. Zhou, J., Tung, A.K., Wu, W., Ng, W.S.: A semi-Lazy?approach to probabilistic path prediction in dynamic environments. In: KDD, pp. 748–756. ACM (2013)

  34. Zhou, N., Zhao, W.X., Zhang, X., Wen, J.R., Wang, S.: A general multi-context embedding model for mining human trajectory data. TKDE 28(8), 1945–1958 (2016)

    Google Scholar 

  35. Zhu, J., Jiang, W., Liu, A., Liu, G., Zhao, L.: Effective and efficient trajectory outlier detection based on time-dependent popular route. WWW 20(1), 111–134 (2017)

    Article  Google Scholar 

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This work was supported in part by the National Natural Science Foundation of China under Grant No. 61572289, and the NSERC Discovery Grants.

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Correspondence to Xiaohui Yu.

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This article belongs to the Topical Collection: Special Issue on Social Computing and Big Data Applications

Guest Editors: Xiaoming Fu, Hong Huang, Gareth Tyson, Lu Zheng, and Gang Wang

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Chen, M., Yu, X. & Liu, Y. MPE: a mobility pattern embedding model for predicting next locations. World Wide Web 22, 2901–2920 (2019).

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  • Human mobility patterns
  • Embedding learning
  • Traffic trajectory data
  • Next location prediction