Can You Trust Online Ratings? A Mutual Reinforcement Model for Trustworthy Online Rating Systems

Hyun Kyo Oh, Sang Wook Kim, Sunju Park, Ming Zhou

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The average of customer ratings on a product, which we call a reputation, is one of the key factors in online purchasing decisions. There is, however, no guarantee of the trustworthiness of a reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of a reputation being manipulated by unfair ratings and design a general framework that provides trustworthy reputations. For this purpose, we propose TRUE-REPUTATION, an algorithm that iteratively adjusts a reputation based on the confidence of customer ratings. We also show the effectiveness of TRUE-REPUTATION through extensive experiments in comparisons to state-of-the-art approaches.

Original languageEnglish
Article number7083723
Pages (from-to)1564-1576
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume45
Issue number12
DOIs
Publication statusPublished - 2015 Dec

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Purchasing
Reinforcement
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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Can You Trust Online Ratings? A Mutual Reinforcement Model for Trustworthy Online Rating Systems. / Oh, Hyun Kyo; Kim, Sang Wook; Park, Sunju; Zhou, Ming.

In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 45, No. 12, 7083723, 12.2015, p. 1564-1576.

Research output: Contribution to journalArticle

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