A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2011.05614v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<span class="release-stage" >pre-print</span>
Recommender System (RS) is currently an effective way to solve information overload. To meet users' next click behavior, RS needs to collect users' personal information and behavior to achieve a comprehensive and profound user preference perception. However, these centrally collected data are privacy-sensitive, and any leakage may cause severe problems to both users and service providers. This paper proposed a novel privacy-preserved recommender system framework (PPRSF), through the application<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.05614v1">arXiv:2011.05614v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rpb227km3bb3dhr3hwjj6rgv5q">fatcat:rpb227km3bb3dhr3hwjj6rgv5q</a> </span>
more »... of federated learning paradigm, to enable the recommendation algorithm to be trained and carry out inference without centrally collecting users' private data. The PPRSF not only able to reduces the privacy leakage risk, satisfies legal and regulatory requirements but also allows various recommendation algorithms to be applied.
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