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Modeling Preferences in a Distributed Recommender System

机译:分布式推荐系统中的建模首选项

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摘要

A good way to help users finding relevant items on document platforms consists in suggesting content in accordance with their preferences. When implementing such a recommender system, the number of potential users and the confidential nature of some data should be taken into account. This paper introduces a new P2P recommender system which models individual preferences and exploits them through a user-centered filtering algorithm. The latter has been designed to deal with problems of scalability, reactivity, and privacy.
机译:帮助用户在文档平台上找到相关项目的一种好方法是根据自己的喜好建议内容。在实施这种推荐系统时,应考虑潜在用户的数量和某些数据的机密性。本文介绍了一个新的P2P推荐器系统,该系统可以对个人偏好进行建模,并通过以用户为中心的过滤算法来利用它们。后者旨在解决可伸缩性,反应性和隐私性问题。

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