A Robust Reputation System using Online Reviews
- Samsung Electronics
hyunkyo.oh@samsung.com - Stanford University
jongbin@stanford.edu - Yonsei University
boxenju@yonsei.ac.kr - Hanyang University
wook@agape.hanyang.ac.kr
Abstract
Evaluating sellers in an online marketplace is an important yet non-trivial task. Many online platforms such as eBay and Amazon rely on buyer reviews to estimate the reliability of sellers on their platform. Such reviews are, however, often biased by: (1) intentional attacks from malicious users and (2) conflation between a buyer’s perception of seller performance and item satisfaction. Here, we present a novel approach to mitigating these issues by decoupling measures of seller performance and item quality, while reducing the impact of malignant reviews. An extensive simulation study shows that our proposed method can recover seller reputations with high rank correlation even under assumptions of extreme noise.
Key words
reputation, reviews, attacks
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS191122007O
Publication information
Volume 17, Issue 2 (June 2020)
Year of Publication: 2020
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
Portable Document Format
How to cite
Oh, H., Jung, J., Park, S., Kim, S.: A Robust Reputation System using Online Reviews. Computer Science and Information Systems, Vol. 17, No. 2, 487–507. (2020), https://doi.org/10.2298/CSIS191122007O