User Behaviour Analysis for the Merchandises Fairness Evaluation

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    The development of digital technology leads to expansion of advertisements of goods and purchases online. The customer reviews and opinions affect in an essential way on the promotion of products and its reputation. In this paper, for the sake of distinguishing the user behavior in terms of his fairness and bias to the purchased merchandises, the user history has been analyze for extracting crucial features (Extreme rating, product’s goodness, and user’s past reviews). The extracted features help economic companies to exclude biased reviews for the fairness purposes of the evaluation of the product.  In addition, they bring to light the identity of the user according to his past ratings and reviewing on purchased goods. The experiments in the research have shown encouraging results with respect to the values of the extracted features.

     

                     


  • Keywords


    Extreme rating, user reviews, merchandises’ goodness component; formatting; style; styling; key words.

  • References


      [1] Hao Tian and Peifeng Liang, " Improved Recommendations Based on Trust Relationships in Social Networks", Future Internet 2017

      [2] Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa, Aaron N. Richter, and Hamzah Al Najada, " Survey of review spam detection using machine learning techniques", Crawford et al. Journal of Big Data (2015) 2:23.

      [3] Eka Dyar Wahyuni and Arif Djunaidy, " Fake Review Detection From A Product Review Using Modified Method OF Iterative Computation Framework", Web of Conferences, DOI: 10.1051/ MATEC 58 03003 (2016).

      [4] Jindal, Nitin, and Bing Liu. "Opinion spam and analysis." Proceedings of the 2008 international conference on web search and data mining. ACM, 2008.

      [5] Beel, Joeran, and Bela Gipp. "Academic search engine spam and Google Scholar's resilience against it." Journal of electronic publishing 13.3 (2010).

      [6] Carpinter, James, and Ray Hunt. "Tightening the net: A review of current and next-generation spam filtering tools." Computers & Security 25.8 (2006): 566-578.

      [7] Manisha Singh, Lokesh Kumar, and Sapna Sinha, " Model for Detecting Fake or Spam Reviews", (Springer Nature Singapore Pte Ltd. 2018).

      [8] Srijan Kumar, Bryan Hooi, Disha Makhija, " REV2: Fraudulent User Prediction in Rating Platforms", WSDM 2018, February 5–9, 2018, Marina Del Rey, CA,

      [9] Kyungmin Lee1, Juyeon Ham2, Sung-Byung Yang3, and Chulmo Koo, " Can You Identify Fake or Authentic Reviews? A fsQCA Approach", © Springer International Publishing AG 2018

      [10] Rupesh Kumar Dewang and Anil Kumar Singh, " State-of-art approaches for review spammer detection: a survey", J Intell Inf Syst (2018).

      [11] Atefeh Heydari, Mohammad Ali Tavakoli a, Naomie Salim and Zahra Heydari, " Detection of review spam: A survey", Expert Systems with Applications 42 (2015) 3634–3642.

      [12] Han, Jiawei, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.

      [13] Uma, K., and M. Hanumanthappa. "Data Collection Methods and Data Pre-processing Techniques for Healthcare Data Using Data Mining.", International Journal of Scientific & Engineering Research Volume 8, Issue 6, June-2017,

      [14] Atefeh Heydari, Mohammad Ali Tavakoli and Naomie Salim., " Detection of fake opinions using time series", Expert Systems With Applications 58 (2016) 83–92.

      [15] Ioannis Dematis (&), Eirini Karapistoli, and Athena Vakali, " Fake Review Detection via Exploitation of Spam Indicators and Reviewer Behavior Characteristics", © Springer International Publishing AG 2018.

      [16] http://jmcauley.ucsd.edu/data/amazon , 2018


 

View

Download

Article ID: 27968
 
DOI: 10.14419/ijet.v7i4.19.27968




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.