# Context Attentive Bandits: Contextual Bandit with Restricted Context

arXiv: https://arxiv.org/abs/1705.03821 · HTML: https://mlsanity.com/paper/1705.03821/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2017.

## Built on by (most cited first, 14 of 14)

- [Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2110.12175/) (2021)
- [Survey: Multi-Armed Bandits Meet Large Language Models](https://mlsanity.com/paper/2505.13355/) (2025)
- [Linear Bandits with Memory: from Rotting to Rising](https://mlsanity.com/paper/2302.08345/) (2023)
- [Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget](https://mlsanity.com/paper/2106.15808/) (2021)
- [Efficient Algorithms for Learning to Control Bandits with Unobserved Contexts](https://mlsanity.com/paper/2202.00867/) (2022)
- [Multiplayer Information Asymmetric Contextual Bandits](https://mlsanity.com/paper/2503.08961/) (2025)
- [Worst-case Performance of Greedy Policies in Bandits with Imperfect Context Observations](https://mlsanity.com/paper/2204.04773/) (2022)
- [Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach](https://mlsanity.com/paper/2308.10699/) (2023)
- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)
- [Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems](https://mlsanity.com/paper/2202.03167/) (2022)
- [Online Learning with Costly Features in Non-stationary Environments](https://mlsanity.com/paper/2307.09388/) (2023)
- [Multi-armed Bandits with Missing Outcome](https://mlsanity.com/paper/2411.05661/) (2024)
- [NH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty](https://mlsanity.com/paper/2605.01745/) (2026)
- [Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits](https://mlsanity.com/paper/2606.08977/) (2026)

## Abstract (arXiv)

We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling. Herein, we adapt the standard multi-armed bandit algorithm known as Thompson Sampling to take advantage of our restricted context setting, and propose two novel algorithms, called the Thompson Sampling with Restricted Context(TSRC) and the Windows Thompson Sampling with Restricted Context(WTSRC), for handling stationary and nonstationary environments, respectively. Our empirical results demonstrate advantages of the proposed approaches on several real-life datasets