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Accurate Location Prediction of Social‐Users Using mHMM

Ahsan Hussain, Bettahally N. Keshavamurthy, Ravi Prasad K. Jagannath

National Institute of Technology Goa, Ponda, Goa, 403401, India.

* Corresponding Author: Ahsan Hussain,

Intelligent Automation & Soft Computing 2019, 25(3), 473-486.


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 venuepopularities obtained for location-prediction using the proposed method, outperform various other baseline methods.


Cite This Article

A. Hussain, B. N. Keshavamurthy and R. P. K. Jagannath, "Accurate location prediction of social‐users using mhmm," Intelligent Automation & Soft Computing, vol. 25, no.3, pp. 473–486, 2019.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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