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PRME-GTS: A New Successive POI Recommendation Model with Temporal and Social Influences

  • Rubai Mao
  • Zhe Han
  • Zitu Liu
  • Yong LiuEmail author
  • Xingfeng Lv
  • Ping Xuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

Successive point-of-interest (POI) recommendation is an important research task which can recommend new POIs the user has not visited before. However, the existing researches for new successive POI recommendation ignore the integration of time information and social relations information which can improve the prediction of the system. In order to solve this problem, we propose a new recommendation model called PRME-GTS that incorporates social relations and temporal information in this paper. It can models the relations between users, temporal information, points of interest, and social information, which is based on the framework of pair-wise ranking metric embedding. Experimental results on the two datasets demonstrate that employing temporal information and social relations information can effectively improve the performance of the successive point-of-interest (POI) recommendation.

Keywords

Social networks POI recommendation Metric embedding 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 61972135, No. 61602159), the Natural Science Foundation of Heilongjiang Province (No. F201430), the Innovation Talents Project of Science and Technology Bureau of Harbin (No. 2017RAQXJ094, No. 2017RAQXJ131), and the fundamental research funds of universities in Heilongjiang Province, special fund of Heilongjiang University (No. HDJCCX-201608, No. KJCX201815, No. KJCX201816).

References

  1. 1.
    Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new POI recommendation. In: IJCAI 2016, pp. 2069–2075 (2015)Google Scholar
  2. 2.
    Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. In: IJCAI 2013, pp. 2605–2611 (2013)Google Scholar
  3. 3.
    Ye, M., Yin, P., Lee, W.C., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: ACM SIGIR 2011, pp. 325–334 (2011)Google Scholar
  4. 4.
    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: ACM SIGKDD 2014, pp. 831–840 (2014)Google Scholar
  5. 5.
    Ying, H., Chen, L., Xiong, Y., Wu, J.P.: PGRank: personalized geographical ranking for point-of-interest recommendation. In: WWW 2016, pp. 137–138 (2016)Google Scholar
  6. 6.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)Google Scholar
  7. 7.
    Hu, B., Ester, M.: Social topic modeling for point-of-interest recommendation in location-based social networks. In: ICDM 2014, pp. 845–850 (2014)Google Scholar
  8. 8.
    Li, H., Ge, Y., Hong, R., Zhu, H.: Point-of-interest recommendations: learning potential check-ins from friends. In: ACM SIGKDD 2016, pp. 975–984 (2016)Google Scholar
  9. 9.
    Sang, J., Mei, T., Sun, J.T., Xu, C., Li, S.: Probabilistic sequential POIs recommendation via check-in data. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 402–405 (2012)Google Scholar
  10. 10.
    Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: WWW 2010, pp. 811–820 (2010)Google Scholar
  11. 11.
    Zhao, S., Zhao, T., Yang, H., Lyu, M.R., King, I.: STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation. In: AAAI 2016, pp. 315–321 (2016)Google Scholar
  12. 12.
    Zhu, J., Ma, H., Chen, C., Bu, J.: Social recommendation using low-rank semidefinite program. In: AAAI 2011, pp. 158–163 (2011)Google Scholar
  13. 13.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: ACM SIGKDD 2011, pp. 1082–1090 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rubai Mao
    • 1
  • Zhe Han
    • 1
  • Zitu Liu
    • 1
  • Yong Liu
    • 1
    Email author
  • Xingfeng Lv
    • 1
  • Ping Xuan
    • 1
  1. 1.HeiLongJiang UniversityHarbinChina

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