An application of causal bandit to content optimization

By Sameer Kanase, Yan Zhao, Shenghe Xu, Mitchell Goodman, Manohar Mandalapu, Benjamyn Ward, Chan Jeon, Shreya Kamath, Ben Cohen, Yujia Liu, Hengjia Zhang, Yannick Kimmel, Saad Khan, Brent Payne, Patricia Grao
2022
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Amazon encompasses a large number of discrete businesses such as Retail, Advertising, Fresh, Business (B2B e-commerce), and Prime Video, most of which maintain a presence across its e-commerce website. They produce content for our customers that belong to diverse content types such as merchandising (e.g. product recommendations), product advertisements (e.g. sponsored products and display ads), program adoption banners (e.g. Amazon Fresh), and consumption (e.g. Prime Video). When customers visits a web page on the website, it triggers a content allocation process where we determine the specific content to show in regions of customer shopping experience on that web page. Content produced by the aforementioned businesses then needs to be arbitrated during this process. We present a causal bandit based framework to address the problem of content optimization in this context. The framework is responsible for fairly balancing the differing objectives and methods of these businesses, and selecting the right content to display to the customers at the right time. It does so with the goal of improving the overall site-wide customer shopping experience. In this paper, we present our content optimization framework, describe its components, demonstrate the framework’s effectiveness through online randomized experiments, and share learnings from deploying and testing the framework in production.
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