Data-driven robust strategy for guaranteed delivery of targeted display advertising
The uncertainty in impression supply presents a significant challenge to the optimal allocation of advertising resources.To address this uncertainty,this paper proposes a data-driven distributionally robust model for targeted display ad allocation problem.Firstly,a stochastic programming model with chance constraints is formulated,with the objective of maximizing the publisher's revenue and penalizing both the unmet demand and the excess of demand.Second,using historical impression supply data,a data-driven distributionally robust chance-constrained model is established.This model utilizes the Wasserstein ambiguity set to propose an alloca-tion strategy that maximizes the publisher's revenue even under the worst-case distribution of impression supply.Through a conservative approximation,the model can be reformulated as an easy-to-solve mixed-integer programming problem.Finally,large-scale out-of-sample exper-iments are conducted to validate the feasibility,efficiently,and stability of the model and the solving approach.
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