# AdaptEx: A Self-Service Contextual Bandit Platform

arXiv: https://arxiv.org/abs/2308.08650 · HTML: https://mlsanity.com/paper/2308.08650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination](https://mlsanity.com/paper/2107.02237/) (2021)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Thompson Sampling Algorithms for Cascading Bandits](https://mlsanity.com/paper/1810.01187/) (2018)

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

- [TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences](https://mlsanity.com/paper/2409.12972/) (2024)

## Abstract (arXiv)

This paper presents AdaptEx, a self-service contextual bandit platform widely used at Expedia Group, that leverages multi-armed bandit algorithms to personalize user experiences at scale. AdaptEx considers the unique context of each visitor to select the optimal variants and learns quickly from every interaction they make. It offers a powerful solution to improve user experiences while minimizing the costs and time associated with traditional testing methods. The platform unlocks the ability to iterate towards optimal product solutions quickly, even in ever-changing content and continuous "cold start" situations gracefully.