Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
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International Journal of Electronic Commerce
Dynamic Pay-Per-Action Mechanisms and Applications to Online Advertising
Operations Research
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We study the Cost-Per-Action or Cost-Per-Acquisition (CPA) charging scheme in online advertising. In this scheme, instead of paying per click, the advertisers pay only when a user takes a specific action (e.g. fills out a form) or completes a transaction on their websites. We focus on designing efficient and incentive compatible mechanisms that use this charging scheme. We describe a mechanism based on a sampling-based learning algorithm that under suitable assumptions is asymptotically individually rational, asymptotically Bayesian incentive compatible and asymptotically ex-ante efficient. In particular, we demonstrate our mechanism for the case where the utility functions of the advertisers are independent and identically-distributed random variables as well as the case where they evolve like independent reflected Brownian motions.