# Applying Multi-armed Bandit Algorithms to Computational Advertising

arXiv: https://arxiv.org/abs/2011.10919 · HTML: https://mlsanity.com/paper/2011.10919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

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

- [Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits](https://mlsanity.com/paper/2105.14586/) (2021)

## Abstract (arXiv)

Over the last two decades, we have seen extensive industrial research in the area of computational advertising. In this paper, our goal is to study the performance of various online learning algorithms to identify and display the best ads/offers with the highest conversion rates to web users. We formulate our ad-selection problem as a Multi-Armed Bandit problem which is a classical paradigm in Machine Learning. We have been applying machine learning, data mining, probability, and statistics to analyze big data in the ad-tech space and devise efficient ad selection strategies. This article highlights some of our findings in the area of computational advertising from 2011 to 2015.