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Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction

  • Qiang Cui
  • Yuyuan Tang
  • Shu WuEmail author
  • Liang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

Point-of-interest (POI) prediction is a key task in location-based social networks. It captures the user preference to predict POIs. Recent studies demonstrate that spatial influence is significant for prediction. The distance can be converted to a weight reflecting the relevance of two POIs or can be utilized to find nearby locations. However, previous studies almost ignore the correlation between user and distance. When people choose the next POI, they will consider the distance at the same time. Besides, spatial influence varies greatly for different users. In this work, we propose a Distance-to-Preference (Distance2Pre) network for the next POI prediction. We first acquire the user’s sequential preference by modeling check-in sequences. Then, we propose to acquire the spatial preference by modeling distances between successive POIs. This is a personalized process and can capture the relationship in user-distance interactions. Moreover, we propose two preference encoders which are a linear fusion and a non-linear fusion. Such encoders explore different ways to fuse the above two preferences. Experiments on two real-world datasets show the superiority of our proposed network.

Keywords

POI Sequential preference Spatial preference Non-linear 

Notes

Acknowledgment

This work is jointly supported by National Natural Science Foundation of China (61772528), National Key Research and Development Program (2016YFB1001000), National Natural Science Foundation of China (U1435221).

References

  1. 1.
    Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. In: IJCAI, vol. 13, pp. 2605–2611 (2013)Google Scholar
  2. 2.
    Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP, pp. 1724–1734 (2014)Google Scholar
  3. 3.
    Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: SIGKDD, pp. 1555–1564. ACM (2016)Google Scholar
  4. 4.
    Feng, S., Cong, G., An, B., Chee, Y.M.: POI2Vec: geographical latent representation for predicting future visitors. In: AAAI, pp. 102–108 (2017)Google Scholar
  5. 5.
    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
  6. 6.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)Google Scholar
  7. 7.
    He, X., Zhang, H., Kan, M.Y., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: SIGIR, pp. 549–558. ACM (2016)Google Scholar
  8. 8.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  9. 9.
    Jing, H., Smola, A.J.: Neural survival recommender. In: WSDM, pp. 515–524. ACM (2017)Google Scholar
  10. 10.
    Kapoor, K., Sun, M., Srivastava, J., Ye, T.: A hazard based approach to user return time prediction. In: SIGKDD, pp. 1719–1728. ACM (2014)Google Scholar
  11. 11.
    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: SIGIR, pp. 433–442. ACM (2015)Google Scholar
  12. 12.
    Liu, Q., Wu, S., Wang, D., Li, Z., Wang, L.: Context-aware sequential recommendation. In: ICDM, pp. 1053–1058 (2016)Google Scholar
  13. 13.
    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
  14. 14.
    Liu, X., Liu, Y., Li, X.: Exploring the context of locations for personalized location recommendations. In: IJCAI, pp. 1188–1194 (2016)Google Scholar
  15. 15.
    Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. In: NIPS, pp. 2204–2212 (2014)Google Scholar
  16. 16.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)Google Scholar
  17. 17.
    Wang, H., Shen, H., Ouyang, W., Cheng, X.: Exploiting poi-specific geographical influence for point-of-interest recommendation. In: IJCAI, pp. 3877–3883 (2018)Google Scholar
  18. 18.
    Wang, S., Hu, L., Cao, L., Huang, X., Lian, D., Liu, W.: Attention-based transactional context embedding for next-item recommendation. In: AAAI, pp. 2532–2539 (2018)Google Scholar
  19. 19.
    Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Time-aware point-of-interest recommendation. In: SIGIR, pp. 363–372. ACM (2013)Google Scholar
  20. 20.
    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)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.University of Science and Technology BeijingBeijingChina

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