# Latent Contextual Bandits and their Application to Personalized Recommendations for New Users

arXiv: https://arxiv.org/abs/1604.06743 · HTML: https://mlsanity.com/paper/1604.06743/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2016.

## Built on by (most cited first, 18 of 18)

- [Thompson Sampling with a Mixture Prior](https://mlsanity.com/paper/2106.05608/) (2021)
- [Optimal Algorithms for Latent Bandits with Cluster Structure](https://mlsanity.com/paper/2301.07040/) (2023)
- [Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2110.12175/) (2021)
- [Interaction-Grounded Learning](https://mlsanity.com/paper/2106.04887/) (2021)
- [Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support](https://mlsanity.com/paper/2107.11533/) (2021)
- [Partially Observable Contextual Bandits with Linear Payoffs](https://mlsanity.com/paper/2409.11521/) (2024)
- [Online learning in bandits with predicted context](https://mlsanity.com/paper/2307.13916/) (2023)
- [Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportability](https://mlsanity.com/paper/2502.20153/) (2025)
- [Non-Stationary Latent Auto-Regressive Bandits](https://mlsanity.com/paper/2402.03110/) (2024)
- [Information-Gathering in Latent Bandits](https://mlsanity.com/paper/2207.03635/) (2022)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [A Classification View on Meta Learning Bandits](https://mlsanity.com/paper/2504.04505/) (2025)
- [The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems](https://mlsanity.com/paper/2107.00161/) (2021)
- [Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle](https://mlsanity.com/paper/2301.12571/) (2023)
- [Adaptive Interventions with User-Defined Goals for Health Behavior Change](https://mlsanity.com/paper/2311.09483/) (2023)
- [Identifiable Latent Bandits: Leveraging observational data for personalized decision-making](https://mlsanity.com/paper/2407.16239/) (2024)
- [The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems](https://mlsanity.com/paper/2604.26651/) (2026)
- [CUPID in the Model Zoo: Online Matchmaking for Selecting Your Dream LLM](https://mlsanity.com/paper/2606.00846/) (2026)

## Abstract (arXiv)

Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users' interests. In this paper we propose Latent Contextual Bandits. We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users. We show that our approach achieves a better regret bound than existing algorithms. We also demonstrate the benefit of our approach using a large real world dataset and a preliminary user study.