Table of Content

Open Access iconOpen 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, email

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

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



cc 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.
  • 1457

    View

  • 973

    Download

  • 0

    Like

Related articles

Share Link