Guess Who Rated This Movie: Identifying Users Through Subspace Clustering
This addresses privacy concerns and potential improvements in personalized recommendations for online platforms, but appears incremental as it builds on existing subspace clustering methods.
The paper tackled the problem of identifying multiple users sharing a single account in online recommender systems based on their ratings, using a subspace clustering model, and showed that a significant fraction of such accounts can be reliably identified.
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 userprovided 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.