Reputation Measurement and Malicious Feedback Rating Prevention in Web Service Recommendation Systems
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Abstract
Service Computing refers to a flexible computing architecture that packages functionality as a suite of interoperable routines that can be used within multiple, separate systems from several business domains. Service computing requires loose coupling of services with operating systems, and other technologies that underlie applications
The proposed systems identify malicious feedback ratings by assuming the Cumulative Sum Control Chart, and then it diminishes the effect of individual user feedback references retaining the Pearson Correlation Coefficient. This system gives demand to preserve malicious feedback ratings and it suggest a malicious feedback rating prevention scheme employing Bloom filtering to increase the recommendation achievement. The consequences show that the planned measurement method can lessen the deviance of the reputation measurement and improve the success ratio of the Web service recommendation.
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