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Issue No. 05 - Sept.-Oct. (2015 vol. 8)
ISSN: 1939-1374
pp: 755-767
Shangguang Wang , State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Box 187#, No.10, Xi Tu Cheng Road Beijing, China
Zibin Zheng , Department of Computer Science & Engineering, The Chinese University of Hong Kong,
Zhengping Wu , Department of Computer Science and Engineering, University of Bridgeport, 221 University Avenue, Bridgeport,
Michael R. Lyu , Department of Computer Science & Engineering, The Chinese University of Hong Kong,
Fangchun Yang , State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Box 187#, No.10, Xi Tu Cheng Road Beijing, China
ABSTRACT
Web service recommendation systems can help service users to locate the right service from the large number of available web services. Avoiding recommending dishonest or unsatisfactory services is a fundamental research problem in the design of web service recommendation systems. Reputation of web services is a widely-employed metric that determines whether the service should be recommended to a user. The service reputation score is usually calculated using feedback ratings provided by users. Although the reputation measurement of web service has been studied in the recent literature, existing malicious and subjective user feedback ratings often lead to a bias that degrades the performance of the service recommendation system. In this paper, we propose a novel reputation measurement approach for web service recommendations. We first detect malicious feedback ratings by adopting the cumulative sum control chart, and then we reduce the effect of subjective user feedback preferences employing the Pearson Correlation Coefficient. Moreover, in order to defend malicious feedback ratings, we propose a malicious feedback rating prevention scheme employing Bloom filtering to enhance the recommendation performance. Extensive experiments are conducted by employing a real feedback rating data set with 1.5 million web service invocation records. The experimental results show that our proposed measurement approach can reduce the deviation of the reputation measurement and enhance the success ratio of the web service recommendation.
INDEX TERMS
Web services, Quality of service, Peer-to-peer computing, Measurement, Control charts, Monitoring, Accuracy,Pearson Correlation Coefficient, Web service recommendation, feedback rating, reputation, cumulative sum control chart
CITATION
Shangguang Wang, Zibin Zheng, Zhengping Wu, Michael R. Lyu, Fangchun Yang, "Reputation Measurement and Malicious Feedback Rating Prevention in Web Service Recommendation Systems", IEEE Transactions on Services Computing, vol. 8, no. , pp. 755-767, Sept.-Oct. 2015, doi:10.1109/TSC.2014.2320262
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