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 journalArticlepeer-review

20 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

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea (NRF) through the Korean Government under Grant NRF-2014S1A3A2044046, in part by the Ministry of Science, ICT and Future Planning (MSIP), Korea, under Information Technology Research Center Support Program NIPA-2014-H0301-14-1022 supervised by the National IT Industry Promotion Agency (NIPA), in part by Semiconductor Industry Collaborative Project between Hanyang University and Samsung Electronics Company Ltd., and in part by the NRF through the Korean Government (MSIP) under Grant NRF-2014R1A2A1A10054151.

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

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

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