BLC: Private Matrix Factorization Recommenders via Automatic Group Learning

2015/09/18 by Alessandro Checco, Checco, Alessandro, Giuseppe Bianchi +4
Computer Science · Mathematics · Social Sciences · #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data #Recommender Systems and Techniques #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1509.05789

openalex publication_date 2015/09/18 · arxiv created 2017/02/27 · arxiv updated 2017/03/01 · openalex created_date 2022/08/31 · openalex updated_date 2026/07/28

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.

Citations

• Matrix Factorization Techniques for Recommender Systems

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