Applying Multi-armed Bandit Algorithms to Computational Advertising

• URL: http://arxiv.org/abs/2011.10919v1
• Date: Sun, 22 Nov 2020 03:23:13 GMT
• Title: Applying Multi-armed Bandit Algorithms to Computational Advertising
• Authors: Kazem Jahanbakhsh
• Abstract summary: We 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. This article highlights some of our findings in the area of computational advertising from 2011 to 2015.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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