Discovery of Ranking Fraud Using Evidence Aggregation Approach for Mobile Apps

Authors(4) :-Katkade Roshani, Ghane Deepali, Nimse Megha, Jangam Nandini

Nowadays everyone is using smart phone. There is need of various applications to be installed on smart phone. To download application smart phone user have to visit Apps store such as Google Play Store, Apples store etc. When user visit play store then he or she is able to see the various applications list. This list is built on the basis of promotions and advertisements. User doesn't have knowledge about the application (i.e. Whether the application is useful or not.) So user looks at the list and downloads the applications mostly from first page of App Store. But sometimes it happens that the downloaded application won t work or is useless. That means it is fraud in mobile application list. To avoid this fraud, there is a need to develop an application which will list out the useful applications. To list the applications first we are going to find the active period of the application named as leading session. We are also investing the three types of evidences: 1. Ranking based evidence. 2. Rating based evidence. 3. Review based evidence. Using these three evidences finally we are calculating aggregation of these evidences. We Evaluate our application with real world data collected form play store for long time period.

Authors and Affiliations

Katkade Roshani
Department of Computer Engineering, MCERC, Nashik, Maharashtra, India
Ghane Deepali
Department of Computer Engineering, MCERC, Nashik, Maharashtra, India
Nimse Megha
Department of Computer Engineering, MCERC, Nashik, Maharashtra, India
Jangam Nandini
Department of Computer Engineering, MCERC, Nashik, Maharashtra, India

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

  1. Hengshu Zhu, Hui Xiong Discovery of Ranking Fraud for Mobile Apps. IEEE TRANSACTIONS ON KNOWLEDGE ANDDATA ENGINEERING,2013.
  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. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., pp. 993–1022, 2003
  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. A. Klementiev, D. Roth, K. Small, and I. Titov, “Unsupervised rank aggregation with domain-specific expertise,” in Proc. 21st Int. Joint Conf. Artif. Intell., 2009, pp. 1101–1106.
  10. E.-P. Lim,V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw, “Detecting product review spammers use rating behaviors,” in Proc.19thACMInt. Conf. Inform. Knowl. Manage., 2010, pp. 939–948.
  11. 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.
  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.
  15. 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.

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 183-188
Manuscript Number : IJSRST173354
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Katkade Roshani, Ghane Deepali, Nimse Megha, Jangam Nandini, " Discovery of Ranking Fraud Using Evidence Aggregation Approach for Mobile Apps, International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 3, pp.183-188, March-April-2017. Available at doi : 10.32628/IJSRST173354
Journal URL : http://ijsrst.com/IJSRST173354

Article Preview

Follow Us

Contact Us