Abstract
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.
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References
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)
Batagelj, V., Zaversnik, M.: An O(m) algorithm for cores decomposition of networks. Comput. Sci. 1(6), 34–37 (2003)
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)
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)
Guttman, A.: R-Trees: A Dynamic Index Structure for Spatial Searching, vol. 14. ACM, New York (1984)
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)
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)
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)
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)
Li, Y.M., Chou, C.L., Lin, L.F.: A social recommender mechanism for location-based group commerce. Inf. Sci. 274, 125–142 (2014)
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). https://doi.org/10.1007/978-3-642-29035-0_9
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)
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)
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)
Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)
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)
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)
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). https://doi.org/10.1109/TKDE.2018.2797962
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)
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)
Tu, W., Cheung, D.W., Mamoulis, N., Yang, M., Lu, Z.: Activity recommendation with partners. ACM Trans. Web (TWEB) 12(1), 4 (2017)
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)
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)
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)
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)
Zhang, C., Gartrell, M., Minka, T., Zaykov, Y., Guiver, J., et al.: GroupBox: a generative model for group recommendation (2015)
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)
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)
Acknowledgments
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).
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Ma, Y., Yuan, Y., Wang, G., Bi, X., Wang, Y. (2018). Personalized Geo-Social Group Queries in Location-Based Social Networks. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_26
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