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- W4293677151 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." @default.
- W4293677151 created "2022-08-31" @default.
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- W4293677151 date "2015-09-18" @default.
- W4293677151 modified "2023-09-28" @default.
- W4293677151 title "BLC: Private Matrix Factorization Recommenders via Automatic Group Learning" @default.
- W4293677151 doi "https://doi.org/10.48550/arxiv.1509.05789" @default.
- W4293677151 hasPublicationYear "2015" @default.
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