A Robust Reputation System using Online Reviews

Hyun-Kyo Oh1, Jongbin Jung2, Sunju Park3 and Sang-Wook Kim4

  1. Samsung Electronics
    hyunkyo.oh@samsung.com
  2. Stanford University
    jongbin@stanford.edu
  3. Yonsei University
    boxenju@yonsei.ac.kr
  4. 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

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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