  It is often the case that, within an online recommender system, multiple
users share a common account. Can such shared accounts be identified solely on
the basis of the user- provided ratings? Once a shared account is identified,
can the different users sharing it be identified as well? Whenever such user
identification is feasible, it opens the way to possible improvements in
personalized recommendations, but also raises privacy concerns. We develop a
model for composite accounts based on unions of linear subspaces, and use
subspace clustering for carrying out the identification task. We show that a
significant fraction of such accounts is identifiable in a reliable manner, and
illustrate potential uses for personalized recommendation.
