Exploiting Context Graph Attention for POI Recommendation in Location-Based Social Networks

  • Siyuan Zhang
  • Hong Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


The prevalence of mobile devices and the increasing popularity of location-based social networks (LBSNs) generate a large volume of user mobility data. As a result, POI recommendation systems, which play a vital role in connecting users and POIs, have received extensive attention from both research and industry communities in the past few years. The challenges of POI recommendation come from the very sparse user check-in records with only positive feedback and how to integrate heterogeneous information of users and POIs. The state-of-the-art methods usually exploit the social influence from friends and geographical influence from neighboring POIs for recommendation. However, there are two drawbacks that hinder their performance. First, they cannot model the different degree of influence from different friends to a user. Second, they ignore the user check-ins as context information for preference modeling in the collaborative filtering framework.

To address the limitations of existing methods, we propose a Context Graph Attention (CGA) model, which can integrate context information encoded in different context graphs with the attention mechanism for POI recommendation. CGA first uses two context-aware attention networks to learn the influence weights of different friends and neighboring POIs respectively. At the same time, it applies a dual attention network, which considers the mutual influence of context POIs for a user and the context users for a POI, to learn the influence weights of different context vertices in the user-POI context graph. A multi-layer perceptron integrates the context vectors of users and POIs for estimating the visiting probability of a user to a POI. To the best of our knowledge, this is the first work that applies the attention mechanism for POI recommendation. Extensive experiments on two public check-in data sets show that CGA can outperform the state-of-the-art methods as well as other attentive collaborative filtering methods substantially.


POI recommendation Context graph attention Neural network 


  1. 1.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)Google Scholar
  2. 2.
    Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. GeoInformatica 19(3), 525–565 (2015)CrossRefGoogle Scholar
  3. 3.
    Chen, J., Zhang, H., He, X., Nie, L., Liu, W., Chua, T.S.: Attentive collaborative filtering: multimedia recommendation with item-and component-level attention. In: SIGIR, pp. 335–344 (2017)Google Scholar
  4. 4.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: SIGKDD, pp. 1082–1090 (2011)Google Scholar
  5. 5.
    Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new POI recommendation. In: IJCAI. pp. 2069–2075 (2015)Google Scholar
  6. 6.
    He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)Google Scholar
  7. 7.
    Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T.-S.: Attentional factorization machines: learning the weight of feature interactions via attention networks. In: IJCAI, pp. 3119–3125 (2017)Google Scholar
  8. 8.
    Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-N recommender systems. In: SIGKDD, pp. 659–667 (2013)Google Scholar
  9. 9.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)Google Scholar
  10. 10.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD, pp. 426–434 (2008)Google Scholar
  11. 11.
    Li, H., Ge, Y., Hong, R., Zhu, H.: Point-of-interest recommendations: learning potential check-ins from friends. In: SIGKDD, pp. 975–984 (2016)Google Scholar
  12. 12.
    Li, X., Cong, G., Li, X.L., Pham, T.A.N., Krishnaswamy, S.: Rank-geoFM: a ranking based geographical factorization method for point of interest recommendation. In: SIGIR, pp. 433–442 (2015)Google Scholar
  13. 13.
    Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: SIGKDD, pp. 831–840 (2014)Google Scholar
  14. 14.
    Liu, X., Liu, Y., Li, X.: Exploring the context of locations for personalized location recommendations. In: IJCAI, pp. 1188–1194 (2016)Google Scholar
  15. 15.
    Liu, Y., Pham, T.A.N., Cong, G., Yuan, Q.: An experimental evaluation of point-of-interest recommendation in location-based social networks. VLDB 10(10), 1010–1021 (2017)Google Scholar
  16. 16.
    Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood characteristics for location recommendation. In: CIKM, pp. 739–748 (2014)Google Scholar
  17. 17.
    Zhang, Q., Wang, J., Huang, H., Huang, X., Gong, Y.: Hashtag recommendation for multimodal microblog using co-attention network. In: IJCAI, pp. 3420–3426 (2017)Google Scholar
  18. 18.
    Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based POI embedding for location-based recommendation. In: CIKM, pp. 15–24 (2016)Google Scholar
  19. 19.
    Xiong, C., Callan, J., Liu, T.Y.: Word-entity duet representations for document ranking. In: SIGIR, pp. 763–772 (2017)Google Scholar
  20. 20.
    Yang, C., Bai, L., Zhang, C., Yuan, Q., Han, J.: Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: SIGKDD, pp. 1245–1254 (2017)Google Scholar
  21. 21.
    Ye, M., Yin, P., Lee, W.C., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR, pp. 325–334 (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Chinese University of Hong KongHong KongChina

Personalised recommendations