Abstract
The article outlines a framework for online advertising budget allocation. First, it explores the empirical Bayes methodology for learning the effectiveness of different online ad placements – from historical data of varying quality. Second, it describes an analytical procedure for optimal budget allocation, which builds on risk management and reinforcement learning techniques.
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