SELECTING DIGITAL ADVERTISING RECOMMENDATION POLICIES IN LIGHT OF RISK AND EXPECTED RETURN

摘要

Systems and methods for selecting optimal policies that maximize expected return subject to given risk tolerance and confidence levels. In particular, methods and systems for selecting an optimal ad recommendation policy—based on user data, a set of ad recommendation policies, and risk thresholds—by sampling the user data and estimating gradients. The system or methods utilize the estimated gradients to select a good ad recommendation policy (an ad recommendation policy with high expected return) subject to the risk tolerance and confidence levels. To assist in selecting a risk-sensitive ad recommendation policy, a gradient-based algorithm is disclosed to find a near-optimal policy for conditional-value-at-risk (CVaR) risk-sensitive optimization.

主权项

1. In a digital medium environment for identifying and deploying potential digital advertising campaigns, where campaigns can be altered, removed, or replaced on demand, a method of using risk-sensitive, lifetime value optimization to select ad recommendation policies comprising:
receiving a risk-tolerance value; identifying a set of user data indicating prior behavior in relation to advertising; determining, by one or more processors, an optimized ad recommendation policy that is subject to the risk-tolerance value by simultaneously converging a policy parameter and a risk parameter to identify the optimized ad recommendation policy that is subject to the risk-tolerance value by repeatedly:
estimating a gradient for the policy parameter by sampling the set of user data;estimating a gradient for the risk parameter by sampling the set of user data;using the estimated gradient for the risk parameter to select an updated risk parameter; andusing the estimated gradient for the policy parameter to select an updated policy parameter.