A trust model for recommender agent systems

This paper aims to improve trust models in multi-agent systems based on four vital components, namely: reliability, similarity, satisfaction and trust transitivity. A number of different methods of computing these components were analyzed by considering the most representative existing trust models. The four trust components were identified from existing models then a trust model named trust transitivity-satisfaction-similarity-reliability (TtSSR) was proposed based on these components. TtSSR applied fuzzy logic for computing the identified components. Then by integrating the identified components and using Technique for Order of Preference by Similarity to Ideal Solution as a decision-making method, TtSSR selects the most trustworthy provider agent. The performance of TtSSR was compared with existing trust models using a simulator, specifically with Bayesian Network Model, Probability Certainty Distribution Model, and Dynamic Trust Model which are based on probability and TREPPS which is based on fuzzy logic. The experimental results revealed that TtSSR can significantly improve the accuracy of trust models; while the result of simulations demonstrated that the average accuracy of TtSSR in selecting a trustworthy agent is better than other models. Generally, the results indicated that when these four components were integrated, they performed significantly better in selecting a trustworthy agent as compared to other models.