In today’s connected world it is possible and very common to interact with unknown people, whose reliability is unknown. Trust Metrics are a recently proposed technique for answering questions such as “Should I trust this user?”. However, most of the current research assumes that every user has a global quality score and that the goal of the technique is just to predict this correct value. We show, on data from a real and large user community, Epinions.com, that such an assumption is not realistic because there is a significant portion of what we call controversial users, users who are trusted and distrusted by many. A global agreement about the trustworthiness value of these users cannot exist. We argue, using computational experiments, that the existence of controversial users (a normal phenomena in societies) demands Local Trust Metrics, techniques able to predict the trustworthiness of an user in a personalized way, depending on the very personal view of the judging user.
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