Predicting Early Reviews for Effective Product Marketing on E-Commerce Websites

Authors(4) :-F. Femila, S. Janakipriya, R. B. Nivetha Sruthi, S. Rohini

The percentage of purchasing products by the user has been increased drastically through web. Users even have the facility of sharing their thoughts about the particular product on web in the form of reviews, blogs, comments etc. Many users read review information given on web to take decisions for buying products. Some users may give the reviews for hyping the sale of the product or to decrease the sale. This may confuse the customers who rely on the reviews to buy a product. So, there is a need to find the honest reviews and remove fake reviews that are added by malicious or fraud user. The proposed system comes up with the solution for this problem. Leading events has been used to find the time interval between the reviews. The proposed system mines the active periods such as leading sessions to accurately locate the hierarchical fraud. These leading sessions can be useful for detecting the local anomaly instead of global anomaly of product reviews. After this to analyze the rating, reviews and hierarchy of the product we examine three facts, they are rating based facts, review based facts and hierarchy facts. In addition, we propose an optimization-based aggregation method to integrate all the facts for fraud detection. The evaluations of this optimization are done on synthetic dataset that are collected. The classified and summarized product review information helps web users to understand review contents easily in a short time.

Authors and Affiliations

F. Femila
Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
S. Janakipriya
UG Scholar, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
R. B. Nivetha Sruthi
UG Scholar, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
S. Rohini
UG Scholar, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

Arduino, Wi-Fi (ESP 8266), Load cell, Database System

  1. X. Rong and Q. Mei, “Diffusion of innovations revisited: from social network to innovation network,” in CIKM, 2013, pp. 499- 508.
  2. I. Mele, F. Bonchi, and A. Gionis, “The early-adopter graph and its application to web-page recommendation,” in CIKM, 2012, pp. 1682-1686.
  3. Y.-F. Chen, “Herd behavior in purchasing books online,” Comput-ers in Human Behavior, vol. 24(5), pp. 1977-1992, 2008.
  4. Banerjee, “A simple model of herd behaviour,” Quarterly Journal of Economics, vol. 107, pp. 797-817, 1992.
  5. A. S. E, “Studies of independence and conformity: I. a minority of one against a unanimous majority,” Psychological monographs: General and applied, vol. 70(9), p. 1, 1956.
  6. T. Mikolov, K. Chen, G. S. Corrado, and J. Dean, “Efficient estima-tion of word representations in vector space,” in ICLR, 2013.
  7. A. Bordes, N. Usunier, A. Garc´?a-Duran,´ J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in NIPS, 2013, pp. 2787-2795.
  8. A. S. E, “Studies of independence and conformity: I. a minority of one against a unanimous majority,” Psychological monographs: General and applied, vol. 70(9), p. 1, 1956.
  9. M. L. S. D. X. W. L. S. Mingliang Chen, Qingguo Ma, “The neural and psychological basis of herding in purchasing books online: an event-related potential study,” Cyberpsychology, Behavior, and Social Networking, vol. 13(3), pp. 321-328, 2010.
  10. V. G. D. W. Shih-Lun Tseng, Shuya Lu, “The effect of herding behavior on online review voting participation,” in AMCIS, 2017.
  11. S. M. Mudambi and D. Schuff, “What makes a helpful online review? a study of customer reviews on amazon.com,” in MIS Quarterly, 2010, pp. 185-200.
  12. J. J. Mc Auley, R. Pandey, and J. Leskovec, “Inferring networks of substitutable and complementary products.” in KDD, 2015, pp. 785-794.
  13. E. Gilbert and K. Karahalios, “Understanding deja reviewers.” in CSCW, 2010, pp. 225-228.
  14. E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw, “Detecting product review spammers using rating behaviors,” in CIKM, 2010, pp. 939-948.
  15. C. Wang and D. M. Blei, “Collaborative topic modeling for recom-mending scientific articles,” in SIGKDD, 2011, pp. 448-456.
  16. R. Herbrich, T. Minka, and T. Graepel, “Trueskill: A bayesian skill rating system,” in NIPS, 2006, pp. 569-576.
  17. J. Liu, Y.-I. Song, and C.-Y. Lin, “Competition-based user expertise score estimation,” in SIGIR, 2011, pp. 425-434.
  18. Q. V. Le and T. Mikolov, “Distributed representations of sentences and documents,” in ICML, 2014, pp. 1188-1196.
  19. Y. B. Xavier Glorot, “Understanding the difficulty of training deep feedforward neural networks,” in AISTATS, 2010, pp. 249-256.
  20. R.A.Bradley and M.E.Terry, “Rank analysis of incomplete block designs: I. the method of paired comparisons,” in Biometrika, 1952, pp. 324-345.

Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 290-300
Manuscript Number : CSEIT195216
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

F. Femila, S. Janakipriya, R. B. Nivetha Sruthi, S. Rohini, "Predicting Early Reviews for Effective Product Marketing on E-Commerce Websites ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.290-300, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195216
Journal URL : http://ijsrcseit.com/CSEIT195216

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