Home > Archive > 2019 > Volume 9 Number 5 (Oct. 2019) >
IJMLC 2019 Vol.9(5): 615-620 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.848

Point-of-Interest Based Classification of Similar Users by Using Support Vector Machine and Status Homophily

K. Mohan Kumar and B. Srinivasan

Abstract—Online social networks (OSN) are becoming an indispensable part of everyday life. Apart from allowing various users to perform a vast amount of conventional sharing per second, the networks also provide location-based services, like sharing of location, traveling activities, and performing check-ins. These new dimensions changed the traditional OSN into location-based social networks (LBSN). This paper proposes a novel approach for finding similar users, based on check-ins or points-of-interest (POI) as the key feature and by building a model using a support vector machine (SVM) and status homophily. Furthermore, the performance of the approach and the accuracy of grouping are analyzed in the result analysis section.

Index Terms—Similar users, LBSN, check-ins, POI, support vector machine, status homophily.

The authors are with PG & Research Department of Computer Science, Rajahs Serfoji Govt. College, Thanjavur, India (e-mail: tnjmohankumar@gmail.com, prof.b.srini@gmail.com).

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Cite: K. Mohan Kumar and B. Srinivasan, "Point-of-Interest Based Classification of Similar Users by Using Support Vector Machine and Status Homophily," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 615-620, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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