Guess Who Rated This Movie: Identifying Users Through Subspace Clustering

TL;DR

This paper proposes a subspace clustering approach to identify individual users sharing a common account based solely on their ratings, enhancing personalization and raising privacy considerations.

Contribution

It introduces a novel model for shared accounts using unions of linear subspaces and demonstrates effective user identification through subspace clustering.

Findings

A significant fraction of shared accounts are reliably identifiable.

The method improves personalized recommendations.

The approach raises privacy concerns.

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.