The authors of this work present a model that reduces product rating 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 among reviewers' rating criteria, customer ratings are often biased. For example, a three-star rating can be considered low for an optimistic reviewer. On the other hand, the same three-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.
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November 2017
Research-Article
Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers
Sunghoon Lim,
Sunghoon Lim
Mem. ASME
Industrial and Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: slim@psu.edu
Industrial and Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: slim@psu.edu
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Conrad S. Tucker
Conrad S. Tucker
Mem. ASME
Engineering Design and Industrial and
Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: ctucker4@psu.edu
Engineering Design and Industrial and
Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: ctucker4@psu.edu
Search for other works by this author on:
Sunghoon Lim
Mem. ASME
Industrial and Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: slim@psu.edu
Industrial and Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: slim@psu.edu
Conrad S. Tucker
Mem. ASME
Engineering Design and Industrial and
Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: ctucker4@psu.edu
Engineering Design and Industrial and
Manufacturing Engineering,
The Pennsylvania State University,
University Park, PA 16802
e-mail: ctucker4@psu.edu
1Corresponding author.
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 31, 2017; final manuscript received August 2, 2017; published online October 2, 2017. Assoc. Editor: Yan Wang.
J. Mech. Des. Nov 2017, 139(11): 111409 (11 pages)
Published Online: October 2, 2017
Article history
Received:
March 31, 2017
Revised:
August 2, 2017
Citation
Lim, S., and Tucker, C. S. (October 2, 2017). "Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers." ASME. J. Mech. Des. November 2017; 139(11): 111409. https://doi.org/10.1115/1.4037612
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