Group-Based Personalized Location Recommendation on Social Networks

  • Henan Wang
  • Guoliang Li
  • Jianhua Feng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8709)


Location-based social networks (LBSNs) have attracted significant attention recently, thanks to modern smartphones and Mobile Internet, which make it convenient to capture a user’s location and share users’ locations. LBSNs generate large amount of user generated content (UGC), including both location histories and social relationships, and provide us with opportunities to enable location-aware recommendation. Existing methods focus either on recommendation efficiency at the expense of low quality or on recommendation quality at the cost of low efficiency. To address these limitations, in this paper we propose a group-based personalized location recommendation system, which can provide users with most interested locations, based on their personal preferences and social connections. We adopt a two-step method to make a trade-off between recommendation efficiency and quality. We first construct a hierarchy for locations based on their categories and group users based on their locations and the hierarchy. Then for each user, we identify her most relevant group and use the users in the group to recommend interested locations for the user. We have implemented our method and compared with existing approaches. Experimental results on real-world datasets show that our method achieves good quality and high performance and outperforms existing approaches.


Candidate Location Recommendation Algorithm Location History Skyline Query User Generate Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bao, J., Zheng, Y., Mokbel, M.F.: Location-based and preference-aware recommendation using sparse geo-social networking data. In: SIGSPATIAL/GIS, pp. 199–208 (2012)Google Scholar
  2. 2.
    Berjani, B., Strufe, T.: A recommendation system for spots in location-based online social networks. In: SNS, p. 4 (2011)Google Scholar
  3. 3.
    Chow, C.-Y., Bao, J., Mokbel, M.F.: Towards location-based social networking services. In: GIS-LBSN, pp. 31–38 (2010)Google Scholar
  4. 4.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching, vol. 14 (1984)Google Scholar
  5. 5.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR, pp. 230–237 (1999)Google Scholar
  6. 6.
    Jin, Z., Shi, D., Wu, Q., Yan, H., Fan, H.: Lbsnrank: personalized pagerank on location-based social networks. In: UbiComp, pp. 980–987 (2012)Google Scholar
  7. 7.
    Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)CrossRefGoogle Scholar
  8. 8.
    Kodama, K., Iijima, Y., Guo, X., Ishikawa, Y.: Skyline queries based on user locations and preferences for making location-based recommendations. In: GIS-LBSN, pp. 9–16 (2009)Google Scholar
  9. 9.
    Li, G., Chen, S., Feng, J., Tan, K.-l., Li, W.-S.: Efficient location-aware influence maximization (2014)Google Scholar
  10. 10.
    Li, G., Hu, J., Lee Tan, K., Bao, Z., Feng, J.: Effective location identification from microblogs. In: ICDE (2014)Google Scholar
  11. 11.
    Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.-C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: KDD, pp. 1023–1031 (2012)Google Scholar
  12. 12.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)Google Scholar
  13. 13.
    Xiao, X., Zheng, Y., Luo, Q., Xie, X.: Finding similar users using category-based location history. In: GIS, pp. 442–445 (2010)Google Scholar
  14. 14.
    Yang, D.-N., Shen, C.-Y., Lee, W.-C., Chen, M.-S.: On socio-spatial group query for location-based social networks. In: KDD, pp. 949–957 (2012)Google Scholar
  15. 15.
    Ye, M., Yin, P., Lee, W.-C.: Location recommendation for location-based social networks. In: GIS, pp. 458–461 (2010)Google Scholar
  16. 16.
    Zheng, V.W., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with gps history data. In: WWW, pp. 1029–1038 (2010)Google Scholar
  17. 17.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from gps trajectories. In: WWW, pp. 791–800 (2009)Google Scholar
  18. 18.
    Zheng, Y., Zhou, X. (eds.): Computing with Spatial Trajectories. Springer (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Henan Wang
    • 1
  • Guoliang Li
    • 1
  • Jianhua Feng
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

Personalised recommendations