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Bayesian Counterfactual Risk Minimization Ben London (blondon@) Amazon Music Ted Sandler (sandler@) Amazon Music International Conference on Machine Learning Long Beach, CA, June 11, 2019 Learning from Logged Data Pull log data e.g., user i

  1. Bayesian Counterfactual Risk Minimization Ben London (blondon@) Amazon Music Ted Sandler (sandler@) Amazon Music International Conference on Machine Learning Long Beach, CA, June 11, 2019

  2. Learning from Logged Data Pull log data e.g., user i listened to item j Launch new Design/train if unsuccessful policy new rec policy if successful A/B test new policy vs old policy

  3. Problem 1: Bandit Feedback • Only observe outcomes from actions taken • e.g., only get feedback on recommendations Alexa, play music Here’s a station you might like …

  4. Problem 2: Bias • Logged data is biased • Policy typically not uniform distribution high support → better estimate • User typically doesn’t see everything • Bias affects inferences • Self-fulfilling prophecies; “rich get richer” low support • Miss key insights due to insufficient support → who knows?

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