Eliminating the Effect of Rating Bias on Reputation Systems
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 qua...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4325016 |
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| _version_ | 1832554537749053440 |
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| author | Leilei Wu Zhuoming Ren Xiao-Long Ren Jianlin Zhang Linyuan Lü |
| author_facet | Leilei Wu Zhuoming Ren Xiao-Long Ren Jianlin Zhang Linyuan Lü |
| author_sort | Leilei Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f626517467674d1ea983e3e5ae49260b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-f626517467674d1ea983e3e5ae49260b2025-02-03T05:51:23ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/43250164325016Eliminating the Effect of Rating Bias on Reputation SystemsLeilei Wu0Zhuoming Ren1Xiao-Long Ren2Jianlin Zhang3Linyuan Lü4Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, ChinaAlibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaComputational Social Science, ETH Zurich, Zurich, SwitzerlandAlibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, ChinaThe 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.http://dx.doi.org/10.1155/2018/4325016 |
| spellingShingle | Leilei Wu Zhuoming Ren Xiao-Long Ren Jianlin Zhang Linyuan Lü Eliminating the Effect of Rating Bias on Reputation Systems Complexity |
| title | Eliminating the Effect of Rating Bias on Reputation Systems |
| title_full | Eliminating the Effect of Rating Bias on Reputation Systems |
| title_fullStr | Eliminating the Effect of Rating Bias on Reputation Systems |
| title_full_unstemmed | Eliminating the Effect of Rating Bias on Reputation Systems |
| title_short | Eliminating the Effect of Rating Bias on Reputation Systems |
| title_sort | eliminating the effect of rating bias on reputation systems |
| url | http://dx.doi.org/10.1155/2018/4325016 |
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