# BLC: Private Matrix Factorization Recommenders via Automatic Group Learning

arXiv: https://arxiv.org/abs/1509.05789 · HTML: https://mlsanity.com/paper/1509.05789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits](https://mlsanity.com/paper/2305.18305/) (2023)

## Abstract (arXiv)

We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of "hiding in the crowd" privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or nym) setting and the BLC algorithm for carrying out this matrix factorization in a privacy-enhanced manner. We demonstrate that the increased privacy does not come at the cost of reduced recommendation accuracy.