A robust reputation system using online reviews

Hyun Kyo Oh, Jongbin Jung, Sunju Park, Sang Wook Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 rep-utations with high rank correlation even under assumptions of extreme noise.

Original languageEnglish
Pages (from-to)487-507
Number of pages21
JournalComputer Science and Information Systems
Issue number2
Publication statusPublished - 2020 Jun

Bibliographical note

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All Science Journal Classification (ASJC) codes

  • General Computer Science


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