# Generalized Thompson Sampling for Contextual Bandits

arXiv: https://arxiv.org/abs/1310.7163 · HTML: https://mlsanity.com/paper/1310.7163/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2013.

## Built on by (most cited first, 5 of 5)

- [Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits](https://mlsanity.com/paper/2205.13924/) (2022)
- [Tree Ensembles for Contextual Bandits](https://mlsanity.com/paper/2402.06963/) (2024)
- [Optimal Exploration is no harder than Thompson Sampling](https://mlsanity.com/paper/2310.06069/) (2023)
- [A Scalable Recommendation Engine for New Users and Items](https://mlsanity.com/paper/2209.06128/) (2022)
- [Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype](https://mlsanity.com/paper/2505.16918/) (2025)

## Abstract (arXiv)

Thompson Sampling, one of the oldest heuristics for solving multi-armed bandits, has recently been shown to demonstrate state-of-the-art performance. The empirical success has led to great interests in theoretical understanding of this heuristic. In this paper, we approach this problem in a way very different from existing efforts. In particular, motivated by the connection between Thompson Sampling and exponentiated updates, we propose a new family of algorithms called Generalized Thompson Sampling in the expert-learning framework, which includes Thompson Sampling as a special case. Similar to most expert-learning algorithms, Generalized Thompson Sampling uses a loss function to adjust the experts' weights. General regret bounds are derived, which are also instantiated to two important loss functions: square loss and logarithmic loss. In contrast to existing bounds, our results apply to quite general contextual bandits. More importantly, they quantify the effect of the "prior" distribution on the regret bounds.