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In today's connected world it is possible and very com-mon to interact with unknown people, whose reliabil-ity is unknown. Trust Metrics are a recently proposed technique for answering questions such as \\Should I trust this user?". However, most of the current re-search assumes that every user has a global quality score and that the goal of the technique is just to pre-dict 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 signicant portion of what we call controversial users, users who are trusted and distrusted by many. A global agree-ment about the trustworthiness value of these users cannot exist. We argue, using computational experi-ments, that the existence of controversial users (a nor-mal phenomena in societies) demands Local Trust Met-rics, 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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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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