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Recommender systems predict user preferences based on a range of available information. For systems in which users generate streams of content (e.g., blogs or periodically-updated newsfeeds), users may rate the produced content that they read, and be given accurate predictions about future content they are most likely to prefer. The authors design a distributed mechanism for predicting user ratings that avoids the disclosure of information to a centralized authority or an untrusted third party: users disclose the rating they give to certain content only to the user that produced this content.
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