A Confidence-Aware Interval-based Trust Model

It is a common and useful task in a web of trust to evaluate the trust value between two nodes using intermediate nodes. This technique is widely used when the source node has no experience of direct interaction with the target node, or the direct trust is not reliable enough by itself. If trust is used to support decision-making, it is important to have not only an accurate estimate of trust, but also a measure of confidence in the intermediate nodes as well as the final estimated value of trust. The present paper thus aims to introduce a novel framework for integrated representation of trust and confidence using intervals, which provides two operations of trust interval multiplication and summation. The former is used for computing propagated trust and confidence, whereas the latter provides a formula for aggregating different trust opinions. The properties of the two operations are investigated in details. This study also proposes a time-variant method that considers freshness, expertise level and two similarity measures in confidence estimation. The results indicate that this method is more accurate compared to the existing ones. In this regard, the results of experiments carried out on two well-known trust datasets are reported and analyzed, showing that the proposed method increases the accuracy of trust inference in comparison with the existing methods.