An Iterative Reputation Ranking Method via the Beta Probability Distribution
Ranking user reputation and object quality has drawn increasing attention for online rating systems. By introducing an iterative reputation-allocation process, in this paper, we present an iterative reputation ranking algorithm in terms of the beta probability distribution (IBeta), where the user re...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8567893/ |
| _version_ | 1831541396097466368 |
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| author | Xiao-Lu Liu Shu-Wei Jia |
| author_facet | Xiao-Lu Liu Shu-Wei Jia |
| author_sort | Xiao-Lu Liu |
| collection | DOAJ |
| description | Ranking user reputation and object quality has drawn increasing attention for online rating systems. By introducing an iterative reputation-allocation process, in this paper, we present an iterative reputation ranking algorithm in terms of the beta probability distribution (IBeta), where the user reputation is calculated as the probability that the user will give fair ratings to objects and the high reputation users’ ratings have larger weights in dominating the corresponding quantity of fair/unfair ratings. User reputation is reallocated based on their ratings and the previous reputations. The user reputation and users’ quantities of fair/unfair ratings are iteratively updated until they become stable. The experimental results for the synthetic networks show that both the AUC values and Kendall’s tau <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> of the IBeta algorithm are larger than those generated by the RBPD method with different fractions of random ratings. Moreover, the results for the empirical networks indicate that the presented algorithm is more accurate and robust than the RBPD method when the rating systems are under spamming attacks. This paper provides a further understanding on the role of the probability for the online user reputation identification. |
| first_indexed | 2024-12-17T00:16:03Z |
| format | Article |
| id | doaj.art-c45e88c80cf14b02b32d875e32506b31 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:16:03Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c45e88c80cf14b02b32d875e32506b312022-12-21T22:10:42ZengIEEEIEEE Access2169-35362019-01-01754054710.1109/ACCESS.2018.28855518567893An Iterative Reputation Ranking Method via the Beta Probability DistributionXiao-Lu Liu0https://orcid.org/0000-0002-1003-0006Shu-Wei Jia1School of Economics, Fudan University, Shanghai, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou, ChinaRanking user reputation and object quality has drawn increasing attention for online rating systems. By introducing an iterative reputation-allocation process, in this paper, we present an iterative reputation ranking algorithm in terms of the beta probability distribution (IBeta), where the user reputation is calculated as the probability that the user will give fair ratings to objects and the high reputation users’ ratings have larger weights in dominating the corresponding quantity of fair/unfair ratings. User reputation is reallocated based on their ratings and the previous reputations. The user reputation and users’ quantities of fair/unfair ratings are iteratively updated until they become stable. The experimental results for the synthetic networks show that both the AUC values and Kendall’s tau <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> of the IBeta algorithm are larger than those generated by the RBPD method with different fractions of random ratings. Moreover, the results for the empirical networks indicate that the presented algorithm is more accurate and robust than the RBPD method when the rating systems are under spamming attacks. This paper provides a further understanding on the role of the probability for the online user reputation identification.https://ieeexplore.ieee.org/document/8567893/Online rating systemsuser reputationbeta probability distributioniterative ranking algorithm |
| spellingShingle | Xiao-Lu Liu Shu-Wei Jia An Iterative Reputation Ranking Method via the Beta Probability Distribution IEEE Access Online rating systems user reputation beta probability distribution iterative ranking algorithm |
| title | An Iterative Reputation Ranking Method via the Beta Probability Distribution |
| title_full | An Iterative Reputation Ranking Method via the Beta Probability Distribution |
| title_fullStr | An Iterative Reputation Ranking Method via the Beta Probability Distribution |
| title_full_unstemmed | An Iterative Reputation Ranking Method via the Beta Probability Distribution |
| title_short | An Iterative Reputation Ranking Method via the Beta Probability Distribution |
| title_sort | iterative reputation ranking method via the beta probability distribution |
| topic | Online rating systems user reputation beta probability distribution iterative ranking algorithm |
| url | https://ieeexplore.ieee.org/document/8567893/ |
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