An evidence-based scheme for web service selection

  • Authors:

  • Ping Wang;Kuo-Ming Chao;Chi-Chun Lo;Ray Farmer

  • Affiliations:

  • Department of MIS, Kun Shan University, Tainan, Taiwan 710;Department of MIS, Kun Shan University, Tainan, Taiwan 710;Department of MIS, Kun Shan University, Tainan, Taiwan 710;Department of MIS, Kun Shan University, Tainan, Taiwan 710

  • Venue:
  • Information Technology and Management
  • Year:
  • 2011

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Abstract

Existing studies on the web service selection problem focus mainly on the functional QoS properties of the service rather than the consumer satisfaction and trust aspects. While a good QoS enhances the reputation of a service, different consumers invariably hold differing views of the service contents. Some service reputation approaches primarily consider the consumer's prior experience of the service via opinion feedback system, may neglect the effect of social trust transition in the recommendations of others. As a result, the problem of reaching consensus on the level of consumer trust regarding service becomes one of key issues in service selection. This study proposes a trust-based service selection model to estimate the degree of consumer trust in a particular service based on the consumers' direct experience and indirect recommendation of the service. In the proposed approach, the degree of consumer trust is correctly estimated by extending Dempster---Shafer evidence reasoning theory to the reputation computation using consumers' direct experience and incorporating Jøsang's belief model for solving the trust transition problem in the indirect recommendation of the service. The proposed model effectively enables deception detection by means of existing bodies of evidence, and therefore excludes the fraudulent evidence of malicious evaluators from the selection process. In addition, a quality index is proposed to help third party (TTP) examine the body of evidence and make the outranking result more reliable. Importantly, the quality index is based not only on the confidence degree of the evidence, but also on the support degree, and therefore discovers the effects of intentional negative assessments. The validity of the proposed approach is demonstrated numerically by means of two service selection examples.