Advertisement

Point-Of-Interest Recommendation Using Temporal Orientations of Users and Locations

  • Saeid HosseiniEmail author
  • Lei Thor Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9642)

Abstract

Location Based Social Networks (LBSN) promotes communications among subscribers. Utilizing online check-in data supplied via LBSN, Point-Of-Interest (POI) recommendation systems propose unvisited relevant venues to the users. Various techniques have been designed for POI recommendation systems. However, diverse temporal information has not been studied adequately. From temporal perspective, as visited locations during weekday and weekend are marginally different, we choose weekly intervals to improve effectiveness of POI recommenders. However, our method is also applicable to other similar periodic intervals. People usually visit tourist and leisure spots during weekends and work related places during weekdays. Similarly, some users perform check-ins mostly during weekend, while others prefer weekday predominantly. In this paper, we define a new problem to perform recommendation, based on temporal weekly alignments of users and POIs. We argue that locations with higher popularity should be more influential. Therefore, In order to solve the problem, we develop a probabilistic model which initially detects a user’s temporal orientation based on visibility weights of POIs visited by her. As a step further, we develop a recommender framework that proposes proper POIs to the user according to her temporal weekly preference. Moreover, we take succeeding POI pairs visited by the same user into consideration to develop a more efficient temporal model to handle geographical information. Extensive experimental results on two large-scale LBSN datasets verify that our method outperforms current state-of-the-art recommendation techniques.

Keywords

Point-Of-Interest recommendation Location-Based Social Networks Temporal influence 

Notes

Acknowledgements

The authors wish to thank Prof. Xiaofang Zhou and Prof. Shazia Sadiq from The University of Queensland for their advice. This work is supported by Australian Research Council (ARC). Grant Number DP140103171.

References

  1. 1.
    Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.F.: A survey on recommendations in location-based social networks. Submitted to GeoInformatica (2014)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: Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)Google Scholar
  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.
    Ding, Y., Li, X.: Time weight collaborative filtering. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 485–492. ACM (2005)Google Scholar
  5. 5.
    Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 93–100. ACM (2013)Google Scholar
  6. 6.
    Gao, H., Tang, J., Liu, H.: gSCorr: modeling geo-social correlations for new check-ins on location-based social networks. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1582–1586. ACM (2012)Google Scholar
  7. 7.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM (2010)Google Scholar
  8. 8.
    Hu, B., Ester, M.: Spatial topic modeling in online social media for location recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 25–32. ACM (2013)Google Scholar
  9. 9.
    Hung, C.-C., Peng, W.-C., Lee, W.-C.: Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. VLDB J. 1–24 (2011)Google Scholar
  10. 10.
    Leung, K.W.-T., Lee, D.L., Lee, W.-C.: CLR: a collaborative location recommendation framework based on co-clustering. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 305–314. ACM (2011)Google Scholar
  11. 11.
    Li, X., Xu, G., Chen, E., Zong, Y.: Learning recency based comparative choice towards point-of-interest recommendation. Expert Syst. Appl. 42(9), 4274–4283 (2015)CrossRefGoogle Scholar
  12. 12.
    Liu, B., Xiong, H.: Point-of-interest recommendation in location based social networks with topic and location awareness. SDM 13, 396–404 (2013)Google Scholar
  13. 13.
    Liu, B., Xiong, H., Papadimitriou, S., Fu, Y., Yao, Z.: A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans. Knowl. Data Eng. 27(5), 1167–1179 (2015)CrossRefGoogle Scholar
  14. 14.
    Liu, X., Liu, Y., Aberer, K., Miao, C.: Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 733–738. ACM (2013)Google Scholar
  15. 15.
    Lonardi, J., Patel, P.: Finding motifs in time series. In: Proceedings of the 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)Google Scholar
  16. 16.
    Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  17. 17.
    Rahimi, S.M., Wang, X.: Location recommendation based on periodicity of human activities and location categories. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part II. LNCS, vol. 7819, pp. 377–389. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Ricci, F., Nguyen, Q.N.: Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intell. Syst. 22(3), 22–29 (2007)CrossRefGoogle Scholar
  19. 19.
    Symeonidis, P., Ntempos, D., Manolopoulos, Y.: Recommender Systems for Location-based Social Networks, pp. 35–38. Springer, New York (2014)Google Scholar
  20. 20.
    Tax, D.M., Duin, R.P.: Feature scaling in support vector data descriptions. Technical report (2000)Google Scholar
  21. 21.
    Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. (2006)Google Scholar
  22. 22.
    Wang, C., Ye, M., Lee, W.-C.: From face-to-face gathering to social structure. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 465–474. ACM (2012)Google Scholar
  23. 23.
    Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-sage: a geographical sparse additive generative model for spatial item recommendation, arXiv preprint, arxiv:1503.03650 (2015)
  24. 24.
    Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long-and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723–732. ACM (2010)Google Scholar
  25. 25.
    Ye, M., Yin, P., Lee, W.-C., Lee, D.-L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334. ACM (2011)Google Scholar
  26. 26.
    Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Time-aware point-of-interest recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363–372. ACM (2013)Google Scholar
  27. 27.
    Yuan, Q., Cong, G., Sun, A.: Graph-based point-of-interest recommendation with geographical and temporal influences. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 659–668. ACM (2014)Google Scholar
  28. 28.
    Zhang, J.-D., Chow, C.-Y.: TICRec: a probabilistic framework to utilize temporal influence correlations for time-aware location recommendations (2015)Google Scholar
  29. 29.
    Zhang, Y., Zhang, M., Zhang, Y., Lai, G., Liu, Y., Zhang, H., Ma, S.: Daily-aware personalized recommendation based on feature-level time series analysis. In: Proceedings of the 24th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 1373–1383 (2015)Google Scholar
  30. 30.
    Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1029–1038. ACM (2010)Google Scholar
  31. 31.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM (2009)Google Scholar
  32. 32.
    Zheng, Y., Zhou, X.: Computing with Spatial Trajectories. Springer, New York (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

Personalised recommendations