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BLC: Private Matrix Factorization Recommenders via Automatic Group Learning

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Figure

Fig. 1. Illustrating classical recommender system setup. Each user submits ratings for a set of items, where this set may be different for each user, the ratings being tagged with the user identity, see left-hand plot (a)
Fig. 2. Data flow diagram of the system scenario with two possible points of attack.
Fig. 3. Illustrating partitioning of operations in Algorithm 1 into a private user component and a public sys- sys-tem component, with exchange only of nym-related information between both (no exchange of  individually-identifying user data).
Fig. 4. (a) 2D projection of the positions in the d = 4 dimensional latent space of the users (in blue) and nyms (in red) (b) Prediction RMSE of BLC and classic matrix factorization vs
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