2020/11/22 by Kazem Jahanbakhsh, Jahanbakhsh, Kazem
Business, Management and Accounting · Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Consumer Market Behavior and Pricing #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.2011.10919
openalex publication_date 2020/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.