Can You Trust Online Ratings? A Mutual Reinforcement Model for Trustworthy Online Rating Systems
- Authors
- Oh, Hyun-Kyo; Kim, Sang-Wook; Park, Sunju; Zhou, Ming
- Issue Date
- Dec-2015
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- False reputation; robustness; trust; unfair ratings
- Citation
- IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, v.45, no.12, pp.1564 - 1576
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
- Volume
- 45
- Number
- 12
- Start Page
- 1564
- End Page
- 1576
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/155728
- DOI
- 10.1109/TSMC.2015.2416126
- ISSN
- 2168-2216
- 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.
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