Personalized Geo-Social Group Queries in Location-Based Social Networks

  • Yuliang Ma
  • Ye Yuan
  • Guoren Wang
  • Xin Bi
  • Yishu Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


Geo-social group query, one of the most important issues in LBSNs, combines both location and social factors to generate useful computational results, which is attracting increasing interests from both industrial and academic communities. In this paper, we propose a new type of queries, personalized geo-social group (PGSG) queries, which aim to retrieve both a user group and a venue. Specifically, a PGSG query intends to find a group-venue pattern (consisting of a venue and a group of users with size h), where each user in the group is socially connected with at least c other users in the group and the maximum distance of all the users in the group to the venue is minimized. To tackle the problem of the PGSG query, we propose GVPS, a novel search algorithm to find the optimal user group and venue simultaneously. Moreover, we extend the PGSG query to top-k personalized geo-social group (TkPGSG) query. Instead of finding the optimal solution in the PGSG query, the TkPGSG query is to return multiple feasibility solutions to guarantee the diversity. We propose an advanced search algorithm TkPH to address the TkPGSG query. Comprehensive experimental results demonstrate the efficiency and effectiveness of our proposed approaches in processing the PGSG query and the TkPGSG query on large real-world datasets.



This research is partially funded by the National Natural Science Foundation of China (No. 61572119, 61622202, U1401256, 61732003, 61729201, 61702086) and the Fundamental Research Funds for the Central Universities (No. N150402005).


  1. 1.
    Amer-Yahia, S., Roy, S.B., Chawlat, A., Das, G., Yu, C.: Group recommendation: semantics and efficiency. Proc. VLDB Endow. 2(1), 754–765 (2009)CrossRefGoogle Scholar
  2. 2.
    Batagelj, V., Zaversnik, M.: An O(m) algorithm for cores decomposition of networks. Comput. Sci. 1(6), 34–37 (2003)Google Scholar
  3. 3.
    Cheng, Y., Yuan, Y., Chen, L., Giraud-Carrier, C., Wang, G.: Complex event-participant planning and its incremental variant. In: 2017 IEEE 33rd International Conference on Data Engineering, ICDE, pp. 859–870. IEEE (2017)Google Scholar
  4. 4.
    Fang, Y., Cheng, R., Li, X., Luo, S., Hu, J.: Effective community search over large spatial graphs. Proc. VLDB Endow. 10(6), 709–720 (2017)CrossRefGoogle Scholar
  5. 5.
    Guttman, A.: R-Trees: A Dynamic Index Structure for Spatial Searching, vol. 14. ACM, New York (1984)Google Scholar
  6. 6.
    Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 467–476. ACM (2009)Google Scholar
  7. 7.
    Li, C.T., Shan, M.K.: Team formation for generalized tasks in expertise social networks. In: IEEE Second International Conference on Social Computing, pp. 9–16 (2010)Google Scholar
  8. 8.
    Li, Y., Chen, R., Xu, J., Huang, Q., Hu, H., Choi, B.: Geo-social k-cover group queries for collaborative spatial computing. IEEE Trans. Knowl. Data Eng. 27(10), 2729–2742 (2015)CrossRefGoogle Scholar
  9. 9.
    Li, Y., Wu, D., Xu, J., Choi, B., Su, W.: Spatial-aware interest group queries in location-based social networks. Data Knowl. Eng. 92, 20–38 (2014)CrossRefGoogle Scholar
  10. 10.
    Li, Y.M., Chou, C.L., Lin, L.F.: A social recommender mechanism for location-based group commerce. Inf. Sci. 274, 125–142 (2014)CrossRefGoogle Scholar
  11. 11.
    Liu, W., Sun, W., Chen, C., Huang, Y., Jing, Y., Chen, K.: Circle of friend query in geo-social networks. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7239, pp. 126–137. Springer, Heidelberg (2012). Scholar
  12. 12.
    Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. (TODS) 30(2), 529–576 (2005)CrossRefGoogle Scholar
  13. 13.
    Quijano-Sanchez, L., Recio-Garcia, J.A., Diaz-Agudo, B., Jimenez-Diaz, G.: Social factors in group recommender systems. ACM Trans. Intell. Syst. Technol. (TIST) 4(1), 8 (2013)Google Scholar
  14. 14.
    Quijano-Sanchez, L., Sauer, C., Recio-Garcia, J.A., Diaz-Agudo, B.: Make it personal: a social explanation system applied to group recommendations. Expert Syst. Appl. 76, 36–48 (2017)CrossRefGoogle Scholar
  15. 15.
    Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)MathSciNetCrossRefGoogle Scholar
  16. 16.
    She, J., Tong, Y., Chen, L.: Utility-aware social event-participant planning. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1629–1643. ACM (2015)Google Scholar
  17. 17.
    She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement and its variant for online setting. IEEE Trans. Knowl. Data Eng. 28(9), 2281–2295 (2016)CrossRefGoogle Scholar
  18. 18.
    Tong, Y., Chen, L., Zhou, Z., Jagadish, H.V., Shou, L., Lv, W.: SLADE: a smart large-scale task decomposer in crowdsourcing. IEEE Trans. Knowl. Data Eng. (2018).
  19. 19.
    Tong, Y., She, J., Ding, B., Wang, L., Chen, L.: Online mobile micro-task allocation in spatial crowdsourcing. In: 2016 IEEE 32nd International Conference on Data Engineering, ICDE, pp. 49–60. IEEE (2016)Google Scholar
  20. 20.
    Tong, Y., Wang, L., Zhou, Z., Ding, B., Chen, L., Ye, J., Xu, K.: Flexible online task assignment in real-time spatial data. Proc. VLDB Endow. 10(11), 1334–1345 (2017)CrossRefGoogle Scholar
  21. 21.
    Tu, W., Cheung, D.W., Mamoulis, N., Yang, M., Lu, Z.: Activity recommendation with partners. ACM Trans. Web (TWEB) 12(1), 4 (2017)Google Scholar
  22. 22.
    Yang, D.N., Chen, Y.L., Lee, W.C., Chen, M.S.: On social-temporal group query with acquaintance constraint. Proc. VLDB Endow. 4(6), 397–408 (2011)CrossRefGoogle Scholar
  23. 23.
    Yang, D.N., Shen, C.Y., Lee, W.C., Chen, M.S.: On socio-spatial group query for location-based social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 949–957 (2012)Google Scholar
  24. 24.
    Yuan, Q., Cong, G., Lin, C.Y.: COM: a generative model for group recommendation. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 163–172. ACM (2014)Google Scholar
  25. 25.
    Yuan, Y., Lian, X., Chen, L., Sun, Y., Wang, G.: RSkNN: kNN search on road networks by incorporating social influence. IEEE Trans. Knowl. Data Eng. 28(6), 1575–1588 (2016)CrossRefGoogle Scholar
  26. 26.
    Zhang, C., Gartrell, M., Minka, T., Zaykov, Y., Guiver, J., et al.: GroupBox: a generative model for group recommendation (2015)Google Scholar
  27. 27.
    Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.Y.: Recommending friends and locations based on individual location history. Acm Trans. Web 5(1), 1–44 (2011)CrossRefGoogle Scholar
  28. 28.
    Zhu, Q., Hu, H., Xu, C., Xu, J., Lee, W.C.: Geo-social group queries with minimum acquaintance constraints. VLDB J. 26(5), 709–727 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  3. 3.Sino-Dutch Biomedical and Information Engineering SchoolNortheastern UniversityShenyangChina

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