Location perspective-based neighborhood-aware POI recommendation in location-based social networks

  • Lei GuoEmail author
  • Yufei Wen
  • Fangai Liu
Methodologies and Application


As an effective way to help users find attractive locations and meet their individual needs, point-of-interest (POI) recommendation has become an important application in location-based social networks (LBSNs). Although the geographical influence has been reported as an effective factor for improving POI recommendation accuracy, previous work mainly models it from the user perspective instead of location perspective. Intuitively, neighboring POIs tend to be visited by similar users, which implies that modeling geographical relationships from a location perspective can simulate users’ behavior more reasonably. Moreover, different from traditional recommendation problems, users in LBSNs often express their interests only by checking in different POIs, which is a kind of implicit feedback. In other words, we can easily get the POIs that the users have visited, but it is hard to get the ones that the users do not like. We cannot use a common approach to distinguish these negative values directly. Based on the above observations, this work concentrates on exploiting the geographical relationships among POIs from a location perspective for implicit problem, where a location neighborhood-aware weighted probabilistic matrix factorization is proposed (L-WMF). To be specific, the weighted probabilistic matrix factorization (WMF) that can deal with implicit feedback is first introduced as our basic POI recommendation method. Then, we incorporate the geographical relationships among POIs into the WMF as the regularization terms to exploit the geographical characteristics from a location perspective. Finally, we conduct several experiments to evaluate our L-WMF method on two real-world datasets, and the experimental results indicate that the L-WMF is more effective and can reach better performance than other related methods.


Social network Point-of-interest Weighted matrix factorization Implicit feedback 



This study was funded by the National Natural Science Foundation of China (Nos. 61602282, 61772321), the China Postdoctoral Science Foundation (No. 2016M602181) and the Innovation Foundation of Science and Technology Development Center of Ministry of Education and New H3C Group (No. 2017A15047).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Baral R, Wang D, Li T, Chen SC (2016) Geotecs: exploiting geographical, temporal, categorical and social aspects for personalized poi recommendation (invited paper). In: IEEE international conference on information reuse and integration, pp 94–101Google Scholar
  2. Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. AAAI 12:17–23Google Scholar
  3. Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. In: ACM conference on recommender systems, pp 93–100Google Scholar
  4. Guo L, Ma J, Chen Z, Zhong H (2015a) Learning to recommend with social contextual information from implicit feedback. Soft Comput 19(5):1351–1362CrossRefGoogle Scholar
  5. Guo L, Ma J, Jiang HR, Chen ZM, Xing CM (2015b) Social trust aware item recommendation for implicit feedback. J Comput Sci Technol 30(5):1039–1053MathSciNetCrossRefGoogle Scholar
  6. Guo L, Wen YF, Wang XH (2018) Exploiting pre-trained network embeddings for recommendations in social networks. J Comput Sci Technol 33(4):682–696CrossRefGoogle Scholar
  7. Han J, Yamana H (2017) Geographical diversification in POI recommendation: toward improved coverage on interested areas. In: Proceedings of the eleventh ACM conference on recommender systems, RecSys’17. ACM, pp 224–228Google Scholar
  8. He X, Zhang H, Kan MY, Chua TS (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, SIGIR’16. ACM, pp 549–558Google Scholar
  9. Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: 2008 eighth IEEE international conference on data mining. IEEE, pp 263–272Google Scholar
  10. Li H, Ge Y, Hong R, Zhu H (2016) Point-of-interest recommendations: learning potential check-ins from friends. In: KDD, pp 975–984Google Scholar
  11. Li MR, Huang L, Wang CD (2017) Geographical and overlapping community modeling based on business circles for POI recommendation. In: Sun Y, Lu H, Zhang L, Yang J, Huang H (eds) Intelligence science and big data engineering. Springer, Berlin, pp 665–675CrossRefGoogle Scholar
  12. Li J, Xu W, Wan W, Sun J (2018) Movie recommendation based on bridging movie feature and user interest. J Comput Sci 26:128–134CrossRefGoogle Scholar
  13. Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 831–840Google Scholar
  14. Liu S (2018) User modeling for point-of-interest recommendations in location-based social networks: the state of the art. Mob Inf Syst 2018:1–13Google Scholar
  15. Liu Y, Wei W, Sun A, Miao C (2014) Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, pp 739–748Google Scholar
  16. Pan R, Zhou Y, Cao B, Liu NN, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering. In: 2008 eighth IEEE international conference on data mining. IEEE, pp 502–511Google Scholar
  17. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, pp 452–461Google Scholar
  18. Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. In: Proceedings of the 20th NIPS, NIPS’07. Curran Associates Inc., Red Hook, pp 1257–1264Google Scholar
  19. Si Y, Zhang F, Liu W (2017) CTF-ARA: an adaptive method for POI recommendation based on check-in and temporal features. Knowl Based Syst 128:59–70CrossRefGoogle Scholar
  20. Wei S, Zheng X, Chen D, Chen C (2016) A hybrid approach for movie recommendation via tags and ratings. Electron Commer Res Appl 18:83–94CrossRefGoogle Scholar
  21. Wen Y, Guo L, Chen Z, Ma J (2018) Network embedding based recommendation method in social networks. In: Companion of the the web conference, pp 11–12Google Scholar
  22. Yang D, Zhang D, Yu Z, Wang Z (2013) A sentiment-enhanced personalized location recommendation system. In: Proceedings of the 24th ACM conference on hypertext and social media, HT’13. ACM, pp 119–128Google Scholar
  23. Yang C, Bai L, Zhang C, Yuan Q, Han J (2017) Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 1245–1254Google Scholar
  24. Ye M, Yin P, Lee WC, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 325–334Google Scholar
  25. Yu Y, Yang G, Hao W (2016) A ranking based Poisson matrix factorization model for point-of-interest recommendation. J Comput Res Dev 53(8):1651–1663Google Scholar
  26. Zhang JD, Chow CY (2013) iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 334–343Google Scholar
  27. Zhang JD, Chow CY, Li Y (2015) iGeoRec: a personalized and efficient geographical location recommendation framework. IEEE Trans Serv Comput 8(5):701–714CrossRefGoogle Scholar
  28. Zhang Z, Liu Y, Zhang Z, Shen B (2018) Fused matrix factorization with multi-tag, social and geographical influences for poi recommendation. In: World Wide Web-internet and web information systems, pp 1–16Google Scholar
  29. Zhao S, King I, Lyu MR (2013) Capturing geographical influence in POI recommendations. Lect Notes Comput Sci 8227:530–537CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Postdoctoral Research Station of Management Science and Engineering, School of BusinessShandong Normal UniversityJinanChina
  2. 2.School of BusinessShandong Normal UniversityJinanChina
  3. 3.School of Information Science and EngineeringShandong Normal UniversityJinanChina

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