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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.
FOS: Computer and information sciences, Computer Science - Machine Learning, Matrix factorization, Machine Learning (stat.ML), Clustering, Machine Learning (cs.LG), Privacy, Statistics - Machine Learning, Recommender systems, Settore ING-INF/03 - TELECOMUNICAZIONI, Clustering; Matrix factorization; Privacy; Recommender systems
FOS: Computer and information sciences, Computer Science - Machine Learning, Matrix factorization, Machine Learning (stat.ML), Clustering, Machine Learning (cs.LG), Privacy, Statistics - Machine Learning, Recommender systems, Settore ING-INF/03 - TELECOMUNICAZIONI, Clustering; Matrix factorization; Privacy; Recommender systems
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