Personalized POI Groups Recommendation in Location-Based Social Networks

  • Fei YuEmail author
  • Zhijun Li
  • Shouxu JiangEmail author
  • Xiaofei Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10367)


With development of urban modernization, there are a large number of hop spots covering the entire city, defined as Pionts-of-Interest (POIs) Group consist of POIs. POI Groups have a significant impact on people’s lives and urban planning. Every person has her/his own personalized POI Groups (PPGs) based on preferences and friendship in location-based social networks (LBSNs). However, there are almost no researches on this aspect in recommendation systems. This paper proposes a novel PPGs Recommendation algorithm, and models the PPGs by expanding the model of DBSCAN. Our model considers the degree to each PPG covering the target users’ POI preferences. The system recommends the target user with the PPGs which have the top-N largest scores, and it is one NP-hard problem. This paper proposes the greedy algorithm to solve it. Extensive experiments on the two LBSN datasets illustrate the effectiveness of our proposed algorithm.


POI group recommendation Personalization Geo-social distance Density-based clustering 



This work was supported in part by the National Science Foundation grants NSF-61672196, NSF-61370214, NSF-61300210.


  1. 1.
    Yu, F., Che, N., Li, Z., Li, K., Jiang, S.: Friend recommendation considering preference coverage in location-based social networks. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS, vol. 10235, pp. 91–105. Springer, Cham (2017). doi: 10.1007/978-3-319-57529-2_8 CrossRefGoogle Scholar
  2. 2.
    Ester, M., Kriegel, H.P., Sander, J., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96(34), 226–231 (1996)Google Scholar
  3. 3.
    Shi, J., Mamoulis, N., Wu, D., et al.: Density-based place clustering in geo-social networks. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 99–110. ACM (2014)Google Scholar
  4. 4.
    Li, J.P., Xu, Y., Zhao, L.: OPGs-Rec: organized-POI-groups based recommendation. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) APWeb 2016. LNCS, vol. 9932, pp. 521–524. Springer, Cham (2016). doi: 10.1007/978-3-319-45817-5_56 CrossRefGoogle Scholar
  5. 5.
    Li, Y., Wu, D., Xu, J., et al.: Spatial-aware interest group queries in location-based social networks. In: ACM International Conference on Information and Knowledge Management, pp. 2643–2646. ACM (2012)Google Scholar
  6. 6.
    Wang, X., Donaldson, R., Nell, C., et al.: Recommending groups to users using user-group engagement and time-dependent matrix factorization. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 1331–1337. AAAI Press (2016)Google Scholar
  7. 7.
    Ye, M., Yin, P., Lee, W.C., et al.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceeding of the International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, Beijing, July 2011, pp. 325–334 (2011)Google Scholar
  8. 8.
    Cheng, C., Yang, H., King, I., et al.: Fused matrix factorization with geographical and social influence in location-based social networks. Aaai 12, 17–23 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Compute Science and TechnologyHarbin Institute of TechnologyHarbinChina

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