Eliminating the Effect of Rating Bias on Reputation Systems
Joint Authors
Wu, Leilei
Ren, Zhuoming
Ren, Xiao-Long
Zhang, Jianlin
Lü, Linyuan
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The ongoing rapid development of the e-commercial and interest-base websites makes it more pressing to evaluate objects’ accurate quality before recommendation.
The objects’ quality is often calculated based on their historical information, such as selected records or rating scores.
Usually high quality products obtain higher average ratings than low quality products regardless of rating biases or errors.
However, many empirical cases demonstrate that consumers may be misled by rating scores added by unreliable users or deliberate tampering.
In this case, users’ reputation, that is, the ability to rate trustily and precisely, makes a big difference during the evaluation process.
Thus, one of the main challenges in designing reputation systems is eliminating the effects of users’ rating bias.
To give an objective evaluation of each user’s reputation and uncover an object’s intrinsic quality, we propose an iterative balance (IB) method to correct users’ rating biases.
Experiments on two datasets show that the IB method is a highly self-consistent and robust algorithm and it can accurately quantify movies’ actual quality and users’ stability of rating.
Compared with existing methods, the IB method has higher ability to find the “dark horses,” that is, not so popular yet good movies, in the Academy Awards.
American Psychological Association (APA)
Wu, Leilei& Ren, Zhuoming& Ren, Xiao-Long& Zhang, Jianlin& Lü, Linyuan. 2018. Eliminating the Effect of Rating Bias on Reputation Systems. Complexity،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1134078
Modern Language Association (MLA)
Wu, Leilei…[et al.]. Eliminating the Effect of Rating Bias on Reputation Systems. Complexity No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1134078
American Medical Association (AMA)
Wu, Leilei& Ren, Zhuoming& Ren, Xiao-Long& Zhang, Jianlin& Lü, Linyuan. Eliminating the Effect of Rating Bias on Reputation Systems. Complexity. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1134078
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134078