Semantic inference of user's reputation and expertise to improve collaborative recommendations
Expert Systems with Applications: An International Journal
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Previous trust mechanisms are mainly focused on reputational models based on explicit trust ratings. However, the large amount of system-provided and user-generated content published on Web is often ignored. These information is very important for providing more accurate, fine-grained and efficient trust management. In this paper, we propose a content-based trust mechanism and introduce trust reasoning functionalities which can infer a user’s interests and expertise. We present a new method to calculate user similarity based on their feedbacks on reviews to reduce data sparseness of User Similarity matrix. We pinpoint several characteristics of trust which have been overlooked previously: trust is field-dependant, similarity-sensitive, personalized and etc. we differ various trust levels between pairs of users to support more accurate and efficient trust calculation. Although we choose Epinions as our targeted application, the mechanism proposed in this paper are also applicable to other e-commerce systems.