Amazon.com, Inc.
Dynamic content selection and optimization

Abstract:

Various embodiments of a framework which allow dynamic testing of many creative content and other messages simultaneously using metrics-based optimization. A "multi-armed bandit" algorithmic approach employed, as an alternative to limited AB-type testing, to automatically select a set of content parameters based on the content parameters' respective probabilities, render the selected parameters to generate content sent to a user, and, after obtaining feedback in the form of user interaction data, update the parameters for future, iterative selection of content parameters. This framework can be used in essentially any setting to allow for the provision of feedback, including user interaction data.