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arXiv · 1509.05789

BLC: Private Matrix Factorization Recommenders via Automatic Group Learning

Alessandro Checco·Giuseppe Bianchi·Doug Leith
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Abstract

We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of "hiding in the crowd" privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or nym) setting and the BLC algorithm for carrying out this matrix factorization in a privacy-enhanced manner. We demonstrate that the increased privacy does not come at the cost of reduced recommendation accuracy.

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Alessandro Checco, Giuseppe Bianchi, Doug Leith. 2015-09-18. BLC: Private Matrix Factorization Recommenders via Automatic Group Learning. https://arxiv.org/abs/1509.05789

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