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Accurate Location Prediction of Social‐Users Using mHMM
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
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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
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APA Style
Hussain, A., Keshavamurthy, B.N., Jagannath, R.P.K. (2019). Accurate location prediction of social‐users using mhmm. Intelligent Automation & Soft Computing, 25(3), 473-486. https://doi.org/10.31209/2018.11007092
Vancouver Style
Hussain A, Keshavamurthy BN, Jagannath RPK. Accurate location prediction of social‐users using mhmm. Intell Automat Soft Comput . 2019;25(3):473-486 https://doi.org/10.31209/2018.11007092
IEEE Style
A. Hussain, B.N. Keshavamurthy, and R.P.K. Jagannath, “Accurate Location Prediction of Social‐Users Using mHMM,” Intell. Automat. Soft Comput. , vol. 25, no. 3, pp. 473-486, 2019. https://doi.org/10.31209/2018.11007092
Copyright © 2019 The Author(s). Published by Tech Science Press.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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