Searched for: subject%3A%22Top%255C-N%255C+recommendation%22
(1 - 1 of 1)
document
Slokom, M. (author), Hanjalic, A. (author), Larson, M.A. (author)
In this paper, we propose a new privacy solution for the data used to train a recommender system, i.e., the user–item matrix. The user–item matrix contains implicit information, which can be inferred using a classifier, leading to potential privacy violations. Our solution, called Personalized Blurring (PerBlur), is a simple, yet effective,...
journal article 2021