  Matrix factorization is a popular method to build a recommender system. In
such a system, existing users and items are associated to a low-dimension
vector called a profile. The profiles of a user and of an item can be combined
(via inner product) to predict the rating that the user would get on the item.
One important issue of such a system is the so-called cold-start problem: how
to allow a user to learn her profile, so that she can then get accurate
recommendations?
  While a profile can be computed if the user is willing to rate well-chosen
items and/or provide supplemental attributes or demographics (such as gender),
revealing this additional information is known to allow the analyst of the
recommender system to infer many more personal sensitive information. We design
a protocol to allow privacy-conscious users to benefit from
matrix-factorization-based recommender systems while preserving their privacy.
More precisely, our protocol enables a user to learn her profile, and from that
to predict ratings without the user revealing any personal information. The
protocol is secure in the standard model against semi-honest adversaries.
