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

Guess Who Rated This Movie: Identifying Users Through Subspace Clustering