Research in the area of trust and reputation systems has put a lot of effort in developing various trust models and associated trust update algorithms that support users or their agents with different behavioral profiles. While each work on its own is particularly well suited for a certain user group, it is crucial for users employing different trust representations to have a common understanding about the meaning of a given trust statement.

The contributions of this paper are three-fold: Firstly we present the UniTEC generic trust model that provides a common trust representation for the class of trust update algorithms based on experiences. Secondly, we show how several well-known representative trust-update algorithms can easily be plugged into the UniTEC system, how the mappings between the generic trust model and the algorithm-specific trust models are performed, and most importantly, how our abstraction from algorithm-specific details in the generic trust model enables users using different algorithms to interact with each other and to exchange trust statements. Thirdly we present the results of our comparative evaluation of various trust update algorithms under a selection of test scenarios.