Guess Who Rated This Movie: Identifying Users Through Subspace Clustering

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 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.

10 pages

References in corpus (2)

• A geometric analysis of subspace clustering with outliers
• High-Rank Matrix Completion and Subspace Clustering with Missing Data

Cited by in corpus (5)

• A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data
• Subspace Clustering by Block Diagonal Representation
• Cluster Developing 1-Bit Matrix Completion
• Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes
• Approximate Subspace-Sparse Recovery with Corrupted Data via Constrained ${\ell }_1$-Minimization