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In this paper, the authors propose a novel recommender system which enhances user-based collaborative filtering by using a trust-based social network. Their main idea is to use infinitesimal numbers and polynomials for capturing natural preferences in aggregating opinions of trusted users. They use these opinions to "Help" users who are similar to an active user to come up with recommendations for items for which they might not have an opinion themselves. They argue that the method they propose reflects better the real life behaviour of the people.
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