Trust Service Discovery by Opinions Classification on Virtual Communities

Internet has become an excellent ecommerce platform for bringing together large numbers of buyers and sellers across wide geographic regions. Trust and reputation systems represent a. significant trend in decision support for Internet mediated service provision. However, most existing work assumes that all users have the same trust metrics, but in real life different users often have different preference of product attributes. This paper proposes trusted query navigation model by analyzing online customer reviews of trustworthiness of websites. The first step analyzes counting history feedback in the system offline and generates dynamic trust model by opinion classification. The second step presents trust evaluation ranking to help the user can easily discovery trust service of matching his needs. The experimental evaluation shows that the trusted query evaluation ranking has high trading efficiency, quick learning ability and satisfactory performance.