A new reputation algorithm for evaluating trustworthiness in e-commerce context

Thanks to their ability to detect fraud, poor quality and ill-intentioned feedbacks and scores in online environments, robust Trust Reputation Systems (TRS) provide actionable information to support relying parties taking the right decision in any electronic transaction. In fact, as security providers in e-services, TRS have to faithfully calculate the most trustworthy score for a targeted product or service. Thus, TRS must rely on a robust architecture and suitable algorithms that are able to select, store, generate and classify scores and feedbacks. In this work, we propose a new architecture for TRS in e-commerce application which includes feedbacks' analysis in its treatment of scores. In fact, this architecture is based on an intelligent layer that proposes to each user (i.e. “feedback provider”) who has already given his recommendation, a collection of prefabricated feedbacks summarizing other users' textual feedbacks. A proposed algorithm is used by this architecture in order to calculate the trust degree of the user, the feedback's trustworthiness and generates the global reputation score of the product.