Advertisement

An Attentive Spatio-Temporal Neural Model for Successive Point of Interest Recommendation

  • Khoa D. DoanEmail author
  • Guolei Yang
  • Chandan K. Reddy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

In a successive Point of Interest (POI) recommendation problem, analyzing user behaviors and contextual check-in information in past POI visits are essential in predicting, thus recommending, where they would likely want to visit next. Although several works, especially the Matrix Factorization and/or Markov chain based methods, are proposed to solve this problem, they have strong independence and conditioning assumptions. In this paper, we propose a deep Long Short Term Memory recurrent neural network model with a memory/attention mechanism, for the successive Point-of-Interest recommendation problem, that captures both the sequential, and temporal/spatial characteristics into its learned representations. Experimental results on two popular Location-Based Social Networks illustrate significant improvements of our method over the state-of-the-art methods. Our method is also robust to overfitting compared with popular methods for the recommendation tasks.

Keywords

Deep learning Spatio-temporal data Attention mechanism Recurrent neural network Long short term memory Social networks 

Notes

Acknowledgments

This work was supported in part by the US National Science Foundation grants IIS-1619028, IIS-1707498 and IIS-1838730.

References

  1. 1.
    Bahadori, M.T., Yu, Q.R., Liu, Y.: Fast multivariate spatio-temporal analysis via low rank tensor learning. In: Advances in Neural Information Processing Systems, pp. 3491–3499 (2014)Google Scholar
  2. 2.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  3. 3.
    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)Google Scholar
  4. 4.
    Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In: Advances in Neural Information Processing Systems, pp. 577–585 (2015)Google Scholar
  5. 5.
    Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1555–1564. ACM (2016)Google Scholar
  6. 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 (2015)Google Scholar
  7. 7.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  8. 8.
    Guo, J.: Backpropagation through time. Harbin Institute of Technology, Unpubl. ms (2013)Google Scholar
  9. 9.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  10. 10.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  11. 11.
    Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRefGoogle Scholar
  12. 12.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  13. 13.
    Li, X., Cong, G., Li, X.L., Pham, T.A.N., Krishnaswamy, S.: Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 433–442. ACM (2015)Google Scholar
  14. 14.
    Liu, N.N., Zhao, M., Xiang, E., Yang, Q.: Online evolutionary collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 95–102. ACM (2010)Google Scholar
  15. 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)Google Scholar
  16. 16.
    Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 739–748. ACM (2014)Google Scholar
  17. 17.
    Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
  18. 18.
    Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: Mining user mobility features for next place prediction in location-based services. In: IEEE 12th International Conference on Data Mining, pp. 1038–1043 (2012)Google Scholar
  19. 19.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Nat. Acad. Sci. 88(6), 2297–2301 (1991)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820. ACM (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceVirginia TechArlingtonUSA
  2. 2.Facebook Inc.SeattleUSA

Personalised recommendations