Advertisement

Social Personalized Ranking Embedding for Next POI Recommendation

  • Yan Long
  • Pengpeng ZhaoEmail author
  • Victor S. Sheng
  • Guanfeng Liu
  • Jiajie Xu
  • Jian Wu
  • Zhiming Cui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)

Abstract

As the increasing popularity of the applications of location-based services, points-of-interest (POI) recommendation has become a great value part to help users explore their surrounding living environment and improve the quality of life. Recently, some researchers proposed next POI recommendation, which not only exploiting the users personal interests but also considers the sequential information of users check-ins. There are some next POI recommendation models exploit Metric Embedding method to improve recommendation performance and efficiency. However, these approaches not consider social relations in next POI recommendation, which is challenging due to social relations are noisy and sparse. To this end, in this paper, we proposed a Social Personalized Ranking Embedding (SPRE) model, which integrates user personalization and social relations into consideration, to learn the social relations by social embedding for next POI recommendation. Our experiments on a real-world large-scale dataset (Foursquare) results show that our model outperforms the state-of-the-art next POI recommendation methods.

Keywords

Next POI recommendation Metric embedding Social relations influence 

Notes

Acknowledgments

This work was partially supported by Chinese NSFC project (61472263, 61402312, 61402311, 61472268).

References

  1. 1.
    Chen, S., Moore, J.L., Turnbull, D., Joachims, T.: Playlist prediction via metric embedding. In: ACM Knowledge Discovery and Data Mining, pp. 714–722 (2012)Google Scholar
  2. 2.
    Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: AAAI Conference on Artificial Intelligence (2012)Google Scholar
  3. 3.
    Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendationGoogle Scholar
  4. 4.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090 (2011)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: International Conference on Artificial Intelligence, pp. 2069–2075 (2015)Google Scholar
  6. 6.
    Ference, G., Ye, M., Lee, W.C.: Location recommendation for out-of-town users in location-based social networks. In: ACM International on Conference on Information and Knowledge Management, pp. 721–726 (2013)Google Scholar
  7. 7.
    Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In: ACM Conference on Recommender Systems, pp. 93–100 (2013)Google Scholar
  8. 8.
    Golbeck, J.: Generating predictive movie recommendations from trust in social networks. In: Stølen, K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds.) iTrust 2006. LNCS, vol. 3986, pp. 93–104. Springer, Heidelberg (2006). doi: 10.1007/11755593_8CrossRefGoogle Scholar
  9. 9.
    Hu, B., Ester, M.: Spatial topic modeling in online social media for location recommendation. ACM (2013)Google Scholar
  10. 10.
    Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: ACM SIGKDD, pp. 397–406 (2009)Google Scholar
  11. 11.
    Krohn-Grimberghe, A., Drumond, L., Freudenthaler, C.: Multi-relational matrix factorization using bayesian personalized ranking for social network data. In: ACM International Conference on Web Search and Data Mining, pp. 173–182 (2012)Google Scholar
  12. 12.
    Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Twelfth International Conference on Information and Knowledge Management, pp. 556–559 (2003)Google Scholar
  13. 13.
    Liu, B., Fu, Y., Yao, Z., Xiong, H.: Learning geographical preferences for point-of-interest recommendation. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1043–1051 (2013)Google Scholar
  14. 14.
    Liu, B., Xiong, H.: Point-of-interest recommendation in location based social networks with topic and location awareness (2013)Google Scholar
  15. 15.
    Liu, X., Liu, Y., Aberer, K., Miao, C.: Personalized point-of-interest recommendation by mining users’ preference transition. In: ACM International Conference on Conference on Information Knowledge Management, pp. 733–738 (2013)Google Scholar
  16. 16.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: ACM Conference on Recommender Systems, pp. 17–24 (2007)Google Scholar
  17. 17.
    Rendle, S., Freudenthaler, C.: BPR: bayesian personalized ranking from implicit feedback. In: Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)Google Scholar
  18. 18.
    Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding, pp. 1067–1077Google Scholar
  19. 19.
    Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-sage: a geographical sparse additive generative model for spatial item recommendation. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1255–1264 (2015)Google Scholar
  20. 20.
    Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based POI embedding for location-based recommendation. In: ACM International on Conference on Information and Knowledge Management, pp. 15–24 (2016)Google Scholar
  21. 21.
    Ye, M., Yin, P., Lee, W. C., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR 2011, Beijing, China, pp. 325–334, July 2011Google Scholar
  22. 22.
    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: ACM International Conference on Multimedia, pp. 819–822 (2015)Google Scholar
  23. 23.
    Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: Lcars: a location-content-aware recommender system. In: ACM SIGKDD, pp. 221–229 (2013)Google Scholar
  24. 24.
    Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: ACM International on Conference on Information and Knowledge Management, pp. 1631–1640 (2016)Google Scholar
  25. 25.
    Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Time-aware point-of-interest recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363–372 (2013)Google Scholar
  26. 26.
    Zhang, J.D., Chow, C.Y., Li, Y.: Lore: exploiting sequential influence for location recommendations, pp. 103–112Google Scholar
  27. 27.
    Zhang, Q., Wang, H.: Not all links are created equal: an adaptive embedding approach for social personalized ranking. In: The International ACM SIGIR Conference, pp. 917–920 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yan Long
    • 1
  • Pengpeng Zhao
    • 1
    Email author
  • Victor S. Sheng
    • 2
  • Guanfeng Liu
    • 1
  • Jiajie Xu
    • 1
  • Jian Wu
    • 1
  • Zhiming Cui
    • 1
  1. 1.Department of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Computer Science DepartmentUniversity of Central ArkansasConwayUSA

Personalised recommendations