Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : Dharchana.C.B | Dharshana.L | Srilekha.M | M.Suresh Kumar unnamed

Volume 03 Issue 08 2016

ISSN no:  2348-3121

Page no: 44-47

Abstract – Ranking fraud in the mobile App commerce alludes to false or tricky exercises which have a motivation behind, knocking up the Apps in the reputation list. To be sure, it turns out to be more ceaseless for App designers to utilize shady means, for example, expanding their Apps’ commerce or posting charlatan App evaluations, to confer positioning distortion. While the connotation of avoiding Ranking fraud has been generally perceived, there is constrained comprehension and examination here.This manuscript gives a holistic perspective of positioning falsification and propose a Ranking fraud identification framework for mobile Apps. 

Keywords— Mobile Apps, ranking fraud detection, evidence aggregation, historical ranking, rating, review records 


  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., http://faculty.cs.byu.edu/~ringger/ 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.
  7. A. Klementiev, D. Roth, and K. Small, “An unsupervised learning algorithm for rank aggregation,” in Proc. 18th Eur. Conf. Mach. Learn., 2007, pp. 616–623.
  8. A. Klementiev, D. Roth, and K. Small, “Unsupervised rank aggregation with distance-based models,” in Proc. 25th Int. Conf. Mach. Learn., 2008, pp. 472–479.
  9. E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw, “Detecting product review spammers using rating behaviors,” in Proc. 19thACMInt. Conf. Inform. Knowl. Manage., 2010, pp.
  10. Y.-T. Liu, T.-Y. Liu, T. Qin, Z.-M. Ma, and H. Li, “Supervised rank aggregation,” in Proc. 16th Int. Conf. World Wide Web, 2007, pp. 481– 490.
  11. A. Mukherjee, A. Kumar, B. Liu, J. Wang, M. Hsu, M. Castellanos, and R. Ghosh, “Spotting opinion spammers using behavioral footprints,” in Proc. 19th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2013, pp. 632–640.
  12. A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly, “Detecting spam web pages through content analysis,” in Proc. 15th Int. Conf. World Wide Web, 2006, pp. 83–92.
  13. K. Shi and K. Ali, “Getjar mobile application recommendations with very sparse datasets,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 204–212.
  14. N. Spirin and J. Han, “Survey on web spam detection: Principles and algorithms,” SIGKDD Explor. Newslett., vol. 13, no. 2, pp. 50–64, May 2012.