Applying Multi-armed Bandit Algorithms to Computational Advertising
This work addresses the problem of optimizing ad selection for advertisers and ad platforms by improving conversion rates, representing an incremental contribution to the field.
This paper explores the application of various online learning algorithms, specifically Multi-Armed Bandit (MAB) formulations, to identify and display the most effective advertisements to web users. The goal is to maximize conversion rates, and the paper summarizes findings from industrial research conducted between 2011 and 2015.
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.