Interdependent Model for Point-of-Interest Recommendation via Social Networks

  • Jake Hashim-JonesEmail author
  • Can Wang
  • Md. Saiful Islam
  • Bela Stantic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10837)


Point-of-Interest (POI) recommendation is an important way to help people discover attractive places. POI recommendation approaches are usually based on collaborative filtering methods, whose performances are largely limited by the extreme scarcity of POI check-ins and a lack of rich contexts, and also by assuming the independence of locations. Recent strategies have been proposed to capture the relationship between locations based on statistical analysis, thereby estimating the similarity between locations purely based on the visiting frequencies of multiple users. However, implicit interactions with other link locations are overlooked, which leads to the discovery of incomplete information. This paper proposes a interdependent item-based model for POI recommender systems, which considers both the intra-similarity (i.e. co-occurrence of locations) and inter-similarity (i.e. dependency of locations via links) between locations, based on the TF-IDF conversion of check-in times. Geographic information, such as the longitude and latitude of locations, are incorporated into the interdependent model. Substantial experiments on three social network data sets verify the POI recommendation built with our proposed interdependent model achieves a significant performance improvement compared to the state-of-the-art techniques.



This work was supported by a Summer Scholarship project as well as two Griffith University’s 2018 New Researcher Grants, with Dr. Can Wang and Dr. Md Saiful Islam being Chief Investigators, respectively.


  1. 1.
    Liu, Y., Pham, T.A.N., Cong, G., Yuan, Q.: An experimental evaluation of point-of-interest recommendation in location-based social networks. Proc. VLDB Endow. 10(10), 1010–1021 (2017)CrossRefGoogle Scholar
  2. 2.
    Lu, Z., Dou, Z., Lian, J., Xie, X., Yang, Q.: Content-based collaborative filtering for news topic recommendation. In: 2015 AAAI, pp. 217–223 (2015)Google Scholar
  3. 3.
    Ghazarian, S., Nematbakhsh, M.A.: Enhancing memory-based collaborative filtering for group recommender systems. Expert Syst. Appl. 42(7), 3801–3812 (2015)CrossRefGoogle Scholar
  4. 4.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 42–49 (2009). Scholar
  5. 5.
    Yin, H., Wang, W., Wang, H., Chen, L., Zhou, X.: Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE TKDE 29(11), 2537–2551 (2017)Google Scholar
  6. 6.
    Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.V.H.: Adapting to user interest drift for POI recommendation. IEEE TKDE 28(10), 2566–2581 (2016)Google Scholar
  7. 7.
    Liu, N.N., Yang, Q.: EigenRank: a ranking-oriented approach to collaborative filtering. In: 2008 ACM SIGIR, pp. 83–90 (2008)Google Scholar
  8. 8.
    Zhong, N., Li, Y., Wu, S.T.: Effective pattern discovery for text mining. IEEE TKDE 24(1), 30–44 (2012)Google Scholar
  9. 9.
    Thomas, A., Sindhu, L.: A survey on content based semantic relations in tweets. Int. J. Comput. Appl. 132(11), 14–18 (2015)Google Scholar
  10. 10.
    Sattari, M., Manguoglu, M., Toroslu, I.H., Symeonidis, P., Senkul, P., Manolopoulos, Y.: Geo-activity recommendations by using improved feature combination. In: 2012 Ubicomp, pp. 996–1003 (2012)Google Scholar
  11. 11.
    Vinyals, O., Kaiser, Ł., Koo, T., Petrov, S., Sutskever, I., Hinton, G.: Grammar as a foreign language. In: 2015 NIPS, pp. 2773–2781 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jake Hashim-Jones
    • 1
    Email author
  • Can Wang
    • 1
  • Md. Saiful Islam
    • 1
  • Bela Stantic
    • 1
  1. 1.School of Information and Communication TechnologyGriffith UniversityBrisbaneAustralia

Personalised recommendations