# High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits

arXiv: https://arxiv.org/abs/2305.18305 · HTML: https://mlsanity.com/paper/2305.18305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 3 of 3)

- [Learning to Optimize via Information-Directed Sampling](https://mlsanity.com/paper/1403.5556/) (2014)
- [Latent Bandits Revisited](https://mlsanity.com/paper/2006.08714/) (2020)
- [BLC: Private Matrix Factorization Recommenders via Automatic Group Learning](https://mlsanity.com/paper/1509.05789/) (2015)

## Abstract (arXiv)

We develop a novel latent-bandit algorithm for tackling the cold-start problem for new users joining a recommender system. This new algorithm significantly outperforms the state of the art, simultaneously achieving both higher accuracy and lower regret.