Autosoft Journal

Online Manuscript Access

Accurate Location Prediction of Social-users using mHMM



Prediction space of distinct check-in locations in Location-Based Social Networks is a challenge. In this paper, a thorough analysis of Foursquare Check-ins is done. Based on previous check-in sequences, next location of social-users is accurately predicted using multinomial-Hidden Markov Model (mHMM) with Steady-State probabilities. This information benefits security-agencies in tracking suspects and restaurant-owners to predict their customers' arrivals at different venues on given days. Higher accuracy and Steady-State venue-popularities obtained for location-prediction using the proposed method, outperform various other baseline methods.



Total Pages: 14


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways

Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Online Article

Cite this document


A. Aggarwal, J. Almeida, and P. Kumaraguru. (2013). Detection of spam tipping behaviour on foursquare. In Proceedings of the 22nd International Conference on World Wide Web(pp. 641-648). ACM.

J. Bao, Y. Zheng, and M.F. Mokbel, (2012). Location-based and preference-aware recommendation using sparse geo-social networking data. In Proceedings of the 20th international conference on advances in geographic information systems(pp. 199-208). ACM.

L. E. Baum and J. A. Eagon, (1967). An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology. Bulletin of the American Mathematical Society, 73(3), 360-363.

L. E. Baum, T. Petrie, G. Soules, andN. Weiss.(1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The annals of mathematical statistics,41(1), 164-171.

D. P. Bertsekas and J. N. Tsitsiklis. (2002.). Introduction to probability, (2nd edn.), Belmont, MA: Athena Scientific.

J. A. Bilmes. (1998). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. International Computer Science Institute,4(510), 126.

X. Cao, G. Cong, and C. S. Jensen, (2010). Mining significant semantic locations from GPS data. Proceedings of the VLDB Endowment, 3(1-2), 1009-1020.

O. Cappé, E.Moulines and T. Rydén. (2007). Inference in Hidden Markov Models. In Proceedings of the EUSFLAT Conference(pp. 14-16). Springer.

C. Cheng, H. Yang, I. King, and M. R. Lyu. (2012). Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks. In Aaai(Vol. 12, pp. 17-23).

P. Coppens, C. Veeckman, and L. Claeys. (2015). Privacy in location-based social networks: privacy scripts & user practices. Journal of Location Based Services, 9(1), 1-15.

R. Du, Z.Yu, T. Mei, Z. Wang, Z. Wang, and B. Guo. (2014). Predicting activity attendance in event-based social networks: Content, context and social influence. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing(pp. 425-434). ACM.

G. Farrelly. (2014). Irreplaceable: the role of place information in a location based service.Journal of Location Based Services,8(2), 123-132.

G. D. Forney Jr. (2005). The viterbi algorithm: A personal history. arXiv preprint cs/0504020.Foursquare stats, 2017, check-ins pulse, 2017,

F. Gasparetti. (2017). Personalization and context-awareness in social local search: State-of-the-art and future research challenges. Pervasive and Mobile Computing, 38, 446-473.

S. Gambs, M. O.Killijian, and M. N. del Prado Cortez. (2012). Next place prediction using mobility markov chains. In Proceedings of the First Workshop on Measurement, Privacy, and Mobility(p. 3). ACM.

H. Gao, J. Tang, and H. Liu. (2012). Exploring social-historical ties on location-based social networks. In Icwsm.

H. Hou, L. Jin, Q. Niu, Y. Sun, and M. Lu. (2011). Driver intention recognition method using continuous hidden markov model. International Journal of Computational Intelligence Systems,4(3), 386-393.

T. Lane. (1999), Hidden Markov Models for Human/Computer Interface Modeling. In Proceedings of IJCAI-99 Workshop on Learning about Users(pp. 35-44). Citeseer.

J. Li and L. Li. (2014). A Location Recommender Based on a Hidden Markov Model: Mobile Social Networks. Journal of Organizational Computing and Electronic Commerce, 24(2-3), 257-270

I. Litou, I. Boutsis, and V. Kalogeraki. (2017). Efficient techniques for time-constrained information dissemination using location-based social networks. Information Systems,64, 321-349.

Z. Mao, Y. Jiang, G. Min, S. Leng, X. Jin, and K. Yang. (2017). Mobile social networks: Design requirements, architecture, and state-of-the-art technology. Computer Communications,100, 1-19.

W. Mathew, R. Raposo, and B. Martins. (2012). Predicting future locations with hidden Markov models. In Proceedings of the 2012 ACM conference on ubiquitous computing(pp. 911-918). ACM.

A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. (2011). An empirical study of geographic user activity patterns in foursquare. ICwSM, 11, 70-573.

L. R. Rabiner. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE,77(2), 257-286.

V. Raghavan, G. Ver Steeg, A. Galstyan, and A. G. Tartakovsky. (2014). Modeling temporal activity patterns in dynamic social networks. IEEE Transactions on Computational Social Systems,1(1), 89-107.

G. Sun, Y. Xie, D. Liao, H. Yu, and V. Chang. (2017). User-defined privacy location-sharing system in mobile online social networks. Journal of Network and Computer Applications,86, 34-45.

C. Sutton and A. McCallum. (2006). An introduction to conditional random fields for relational learning (Vol. 2). Introduction to statistical relational learning.MIT Press.

K. Thilakarathna, S. Seneviratne, K. Gupta, M. A. Kaafar, and A. Seneviratne. (2017). A deep dive into location-based communities in social discovery networks. Computer Communications,100, 78-90.

C. R. Vicente, D. Freni, C. Bettini, and C. S. Jensen. (2011). Location-related privacy in geo-social networks. IEEE Internet Computing,15(3), 20-27.

Y. Xiao, X. Lu, and Y. Liu. (2016). A parallel and distributed algorithm for role discovery in large-scale social networks. Intelligent Automation & Soft Computing, 22(4), 675-681.

D. Yang, D. Zhang, B. Qu, and P. Cudré-Mauroux. (2016). PrivCheck: privacy-preserving check-in data publishing for personalized location based services. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing(pp. 545-556). ACM.

D. Yang, D. Zhang, Z. Yu, and Z. Wang. (2013). A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media(pp. 119-128). ACM.

D. Yang, D. Zhang, V. W. Zheng, and Z. Yu. (2015). Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Transactions on Systems, Man, and Cybernetics: Systems,45(1), 129-142.

J. Ye, Z. Zhu, and H. Cheng. (2013). What”s your next move: User activity prediction in location-based social networks. In Proceedings of the 2013 SIAM International Conference on Data Mining(pp. 171-179).

M. Ye, P. Yin, and W. C. Lee. (2010). Location recommendation for location-based social networks. In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems(pp. 458-461). ACM.

M. Ye, P. Yin, W.C.Lee, and D. L. Lee. (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(pp. 325-334). ACM.

B. Zhao, J. He, Y. Zhang, G. Liu, P. Zhai, N. Huang, and R. Liu. (2016). Dynamic trust evaluation in open networks. Intelligent Automation & Soft Computing, 22(4), 631-638.

L. Zhao, Y. Lu, and S.Gupta. (2012). Disclosure intention of location-related information in location-based social network services. International Journal of Electronic Commerce,16(4), 53-90.

Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W. Y. Ma. (2011). Recommending friends and locations based on individual location history. ACM Transactions on the Web (TWEB),5(1), 5


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048