# Guess Who Rated This Movie: Identifying Users Through Subspace Clustering

arXiv: https://arxiv.org/abs/1408.2055 · HTML: https://mlsanity.com/paper/1408.2055/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2014.

## Built on by (most cited first, 4 of 4)

- [Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2206.08088/) (2022)
- [Fusion Subspace Clustering for Incomplete Data](https://mlsanity.com/paper/2205.10872/) (2022)
- [Tweedie Regression for Video Recommendation System](https://mlsanity.com/paper/2505.06445/) (2025)
- [Subspace Clustering on Incomplete Data with Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2602.00262/) (2026)

## Abstract (arXiv)

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.