Hidden location prediction using check-in patterns in location-based social networks

  • Pramit Mazumdar
  • Bidyut Kr. Patra
  • Korra Sathya Babu
  • Russell Lock
Regular Paper


Check-in facility in a location-based social network (LBSN) enables people to share location information as well as real-life activities. Analysing these historical series of check-ins to predict the future locations to be visited has been very popular in the research community. However, it has been found that people do not intend to share the privately visited locations and activities in a LBSN. Research into extrapolating unchecked locations from historical data is limited. Knowledge of hidden locations can have a wide range of benefits to society. It may help the investigating agencies in identifying possible places visited by a suspect, a marketing company in selecting potential customers for targeted marketing, for medical representatives in identifying areas for disease prevention and containment, etc. In this paper, we propose an Associative Location Prediction Model (ALPM), which infers privately visited unchecked locations from a published user trajectory. The proposed ALPM explores the association between a user’s checked-in data, the Hidden Markov Model and proximal locations around a published check-in for predicting the unchecked or hidden locations. We evaluate ALPM on real-world Gowalla LBSN dataset for the users residing in Beijing, China. Experimental results show that the proposed model outperforms the existing state-of-the-art work in the literature.


Location prediction Location-based social networks Ranking Similarity measure Trajectory analysis 


  1. 1.
    Cranshaw J, Toch E, Hong J, Kittur A, Sadeh N (2010) Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM international conference on ubiquitous computing, pp 119–128Google Scholar
  2. 2.
    Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in foursquare. In: Proceedings of the 5th international AAAI conference on weblogs and social media, pp 70–573Google Scholar
  3. 3.
    Sarwat M, Bao J, Eldawy A, Levandoski JJ, Magdy A, Mokbel MF (2012) Sindbad: a location-based social networking system. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data, pp 649–652Google Scholar
  4. 4.
    Backstrom L, Sun E, Marlow C (2010) Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th international conference on world wide web, pp 61–70Google Scholar
  5. 5.
    Scellato Salvatore, Noulas Anastasios, Lambiotte Renaud, Mascolo Cecilia (2011) Socio-spatial properties of online location-based social networks. In: Proceedings of the 5th international AAAI conference on weblogs and social media, pp 329–336Google Scholar
  6. 6.
    Yan Z, Chakraborty D, Parent C, Spaccapietra S, Aberer K (2013) Semantic trajectories: mobility data computation and annotation. ACM Trans Intell Syst Technol (TIST) 4(3):1–49CrossRefGoogle Scholar
  7. 7.
    Erwig M, Ralf Hartmut G, Schneider M, Vazirgiannis M et al (1999) Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica 3(3):269–296CrossRefGoogle Scholar
  8. 8.
    Scellato S, Noulas A, Mascolo C (2011) Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1046–1054Google Scholar
  9. 9.
    Fire M, Tenenboim-Chekina L, Puzis R, Lesser O, Rokach L, Elovici Y (2013) Computationally efficient link prediction in a variety of social networks. ACM Trans Intell Syst Technol (TIST) 5(1):1–10CrossRefGoogle Scholar
  10. 10.
    Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma W-Y (2008) Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 1–34Google Scholar
  11. 11.
    Lee MJ, Chung CW (2011) A user similarity calculation based on the location for social network services. In :Proceedings of the 16th international conference on database systems for advanced applications, pp 38–52Google Scholar
  12. 12.
    Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1082–1090Google Scholar
  13. 13.
    Sadilek A, Kautz H, Bigham JP (2012) Finding your friends and following them to where you are. In :Proceedings of the 5th ACM international conference on web search and data mining, pp 723–732Google Scholar
  14. 14.
    Ying JJC, Lee WC, Tseng VS (2013) Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM Trans Intell Syst Technol (TIST) 5(1):1–33CrossRefGoogle Scholar
  15. 15.
    Huo Z, Meng X, Zhang R (2013) Feel free to check-in: privacy alert against hidden location inference attacks in geosns. In: Proceedings of the 18th international conference on database systems for advanced applications, pp 377–391Google Scholar
  16. 16.
    Robusto CC (1957) The cosine-haversine formula. Am Math Mon 64(1):38–40MathSciNetCrossRefGoogle Scholar
  17. 17.
    Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. ACM SIGMOD Rec 22(2):207–216CrossRefGoogle Scholar
  18. 18.
    Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. ACM SIGMOD Rec 29(2):1–12CrossRefGoogle Scholar
  19. 19.
    Sung HC (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 1(4):300–307Google Scholar
  20. 20.
    Rabiner LR (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286CrossRefGoogle Scholar
  21. 21.
    Forney GD Jr (1973) The viterbi algorithm. Proc IEEE 61(3):268–278MathSciNetCrossRefGoogle Scholar
  22. 22.
    Devijver PA (1985) Baum’s forward-backward algorithm revisited. Pattern Recognit Lett 3(6):369–373CrossRefMATHGoogle Scholar
  23. 23.
    Akoush S, Sameh A (2007) Mobile user movement prediction using bayesian learning for neural networks. In: Proceedings of the 2007 international conference on wireless communications and mobile computing, pp 191–196Google Scholar
  24. 24.
    Zheng VW, Zheng Y, Xie X, Yang Q (2010) Collaborative location and activity recommendations with gps history data. In: Proceedings of the 19th International Conference on World wide web, pp 1029–1038Google Scholar
  25. 25.
    Wang J, De Vries AP, Reinders MJT (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, pp 501–508Google Scholar
  26. 26.
    Ye M, Yin P, Lee W-C (2010) Location recommendation for location-based social networks. In :Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 458–461Google Scholar
  27. 27.
    Shi Y, Karatzoglou A, Baltrunas L, Larson M, Oliver N, Hanjalic A (2012) Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: Proceedings of the 6th ACM conference on recommender systems, pp 139–146Google Scholar
  28. 28.
    Burges CJ, Ragno R, Le QV (2007) Learning to rank with nonsmooth cost functions. In: Schölkopf B, Platt JC, Hoffman T (eds) Advances in neural information processing systems 19, pp 193–200. MIT PressGoogle Scholar
  29. 29.
    Chapelle O, Metlzer D, Zhang Y, Grinspan P (2009) Expected reciprocal rank for graded relevance. In Proceedings of the 18th ACM conference on information and knowledge management, pp 621–630Google Scholar
  30. 30.
    Mazumdar P, Patra BK, Lock R, Korra SB (2016) An approach to compute user similarity for gps applications. Knowl Based Syst 113:125–142CrossRefGoogle Scholar
  31. 31.
    Song C, Zehui Q, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Xin L, Bengtsson L, Holme P (2012) Predictability of population displacement after the 2010 haiti earthquake. Proc Natl Acad Sci 109(29):11576–11581CrossRefGoogle Scholar
  33. 33.
    Xin L, Wetter E, Bharti N, Tatem AJ, Bengtsson L (2013) Approaching the limit of predictability in human mobility. Sci Rep 3(2923):1–9Google Scholar
  34. 34.
    Shaw MJ, Subramaniam C, Tan GW, Welge ME (2001) Knowledge management and data mining for marketing. Decis Support Syst 31(1):127–137CrossRefGoogle Scholar
  35. 35.
    Gutscher A (2006) Coordinate transformation-a solution for the privacy problem of location based services? In :Proceedings of the 20th international conference on parallel and distributed processing, pp 1–7Google Scholar
  36. 36.
    Ardagna CA, Cremonini M, Damiani E, De Capitani DVS, Samarati P (2007) Location privacy protection through obfuscation-based techniques. In Proceedings of the IFIP annual conference on data and applications security and privacy, pp 47–60Google Scholar
  37. 37.
    Gruteser M, Grunwald D (2003) Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of the 1st international conference on mobile systems, applications and services, pp 31–42Google Scholar
  38. 38.
    Gedik B, Liu L (2005) Location privacy in mobile systems: a personalized anonymization model. In :Proceedings of the 25th IEEE international conference on distributed computing systems, pp 620–629Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Pramit Mazumdar
    • 1
  • Bidyut Kr. Patra
    • 1
  • Korra Sathya Babu
    • 1
  • Russell Lock
    • 2
  1. 1.National Institute of Technology RourkelaRourkelaIndia
  2. 2.Loughborough UniversityLeicestershireUK

Personalised recommendations