Mitigating Online Product Rating Biases through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers

[+] Author and Article Information
Sunghoon Lim

Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802

Conrad Tucker

Mem. ASME, Engineering Design and Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802

1Corresponding author.

ASME doi:10.1115/1.4037612 History: Received March 31, 2017; Revised August 02, 2017


The authors of this work present a model that reduces product ratings biases that are a result of varying degrees of customers' optimism/pessimism. Recently, large-scale customer reviews and numerical product ratings have served as substantial criteria for new customers who make their purchasing decisions through electronic word-of-mouth. However, due to differences between reviewers' rating criteria, customer ratings are often biased. For example, a 3-star rating can be considered low for an optimistic reviewer. On the other hand, the same 3-star rating can be considered high for a pessimistic reviewer. Many existing studies of online customer reviews overlook the significance of reviewers' rating histories and tendencies. Considering reviewers' rating histories and tendencies is significant for identifying unbiased customer ratings and true product quality, because each reviewer has different criteria for buying and rating products. The proposed customer rating analysis model adjusts product ratings in order to provide customers with more objective and accurate feedback. The authors propose an unsupervised model aimed at mitigating customer ratings based on rating histories and tendencies, instead of human-labeled training data. A case study involving real-world customer rating data from an electronic commerce company is used to validate the method.

Copyright (c) 2017 by ASME
Your Session has timed out. Please sign back in to continue.






Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In