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Mobile App Recommendation & Ranking Fraud Detection on Relationship among Rating Review & Ranking

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

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Mobile application plays an important role for all the smart phone users to play or perform different tasks. Mobile application developers are available in large number; they can develop the different mobile applications. For making lager users for their applications some developers involve in illegal activities. Due to these illegal activities the mobile applications hires high rank in the application popularity list. Such fraudulent activities are used by more and more application developers. A ranking fraud detection system for mobile Apps is proposed in this paper. Accurately locate the ranking fraud by mining the leading sessions, of mobile Apps.R3-RFD algorithm is proposed in this paper. Furthermore, sentiword dictionary is used to identify the exact reviews scores. The fake feedbacks by a same person for pushing up that app on the leaderboard are restricted. Two different constraints are considered for accepting the feedback given to an application. The first constraint is that an app can be rated only once from a user login. And the second is implemented with the aid of MAC address that limits the number of user login logged per day from a MAC address as five.


[1] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., pp. 993–1022, 2003.

[2] Y. Ge, H. Xiong, C. Liu, and Z.-H. Zhou, “A taxi driving fraud detection system,” in Proc. IEEE 11th Int. Conf. Data Mining, 2011, pp. 181– 190.

[3] D. F. Gleich and L.-h. Lim, “Rank aggregation via nuclear norm minimization,” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2011, pp. 60–68.

[4] T. L. Griffiths and M. Steyvers, “Finding scientific topics,” Proc. Nat. Acad. Sci. USA, vol. 101, pp. 5228–5235, 2004.

[5] G. Heinrich, Parameter estimation for text analysis, “ Univ. Leipzig, Leipzig, Germany, Tech. Rep., CS601R/papers/Heinrich-GibbsLDA.pdf, 2008.

[6] N. Jindal and B. Liu, “Opinion spam and analysis,” in Proc. Int.Conf. Web Search Data Mining, 2008, pp. 219–230.


Mobile Applications, fraud detection, evidences, Historical Record

  • Format Volume 4, Issue 2, No 4, 2016
  • Copyright All Rights Reserved ©2016
  • Year of Publication 2016
  • Author E.Ramya, Mrs.V.Vetriselvi
  • Reference IJCS-131
  • Page No 772-777

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