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Coping with unfair ratings in reputation systems based on learning approach

ABSTRACT One of the problems in service-oriented environments is the existence of deceptive agents and their intentional or unintentional malicious behaviour. A more complex challenge is unpredictable changes that some agents make to their behaviour, which is a cause of performance decrease in trust systems. Many trust models have been developed based on feedback mechanisms, but most of them are unable to detect inaccurate information that a deceptive agent may generate; therefore, the trust systems face difficulties in separating the fair from the unfair agents. In this paper, we used learning automata to separate fair from deceptive agents based on an aggregation of their feedback from the votes on each service attribute. The main contribution of this work was a new scheme for separating fair from unfair agents and for estimating the quality of services. We took a more detailed view of service attributes by decomposing a service to its qualitative elements to reach a better match between recommended service characteristics and user preferences. The experimental simulations showed that the new method outperformed other existing models, especially within open environments, in terms of convergence time, accuracy of trust values for unknown agents, user demand modelling, service selection performance and scalability.