Topic-aware experience trust computation with refined interaction in social networks

Abstract

Computational trust more and more plays an important role in interaction process of users or peers in distributed systems. Most current trust models are constructed based on interaction experience and reputation. Interaction trust is estimated from interaction experience among users, whereas reputation trust is inferred from some commmunity evaluation via propagation mechanisms. However, these reputation models either lack a clear foundation for computation or have no rules for determining community. And these issues deduce to difficulty in the trust implementation and design. Our purpose of this paper is to present a trust model of reputation, which estimates trustworthiness degrees based on similairity and path algebra from community. The similarity measure is resulted from the interest degrees that are formulated by means of analyzing entries data dispatched by users and topics. The path algebra is built from two operators concatenation and aggregation for integrating respectively scores along a path and from various paths. We perform experiments to determine how the path algebra and similarity impact on trust estimation. Our experimental results show that the similarity-based estimation outperforms the path algebra computation.