Guess Who Rated This Movie: Identifying Users Through Subspace Clustering

Amy Zhang, Nadia Fawaz, Stratis Ioannidis, Andrea Montanari
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:943-952, 2012.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-zhang12b, title = {Guess Who Rated This Movie: Identifying Users Through Subspace Clustering}, author = {Zhang, Amy and Fawaz, Nadia and Ioannidis, Stratis and Montanari, Andrea}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {943--952}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/zhang12b/zhang12b.pdf}, url = {https://proceedings.mlr.press/r10/zhang12b.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Guess Who Rated This Movie: Identifying Users Through Subspace Clustering %A Amy Zhang %A Nadia Fawaz %A Stratis Ioannidis %A Andrea Montanari %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-zhang12b %I PMLR %P 943--952 %U https://proceedings.mlr.press/r10/zhang12b.html %V R10 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Zhang, A., Fawaz, N., Ioannidis, S. & Montanari, A.. (2012). Guess Who Rated This Movie: Identifying Users Through Subspace Clustering. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:943-952 Available from https://proceedings.mlr.press/r10/zhang12b.html. Reissued by PMLR on 04 October 2026.

Related Material