Open Access
ARTICLE
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. https://doi.org/10.31209/2018.11007092
Abstract
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.
Keywords
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. https://doi.org/10.31209/2018.11007092