Adversarial Learning for Incentive Optimization in Mobile Payment Marketing. (arXiv:2112.15434v1 [cs.LG])

Many payment platforms hold large-scale marketing campaigns, which allocate
incentives to encourage users to pay through their applications. To maximize
the return on investment, incentive allocations are commonly solved in a
two-stage procedure. After training a response estimation model to estimate the
users' mobile payment probabilities (MPP), a linear programming process is
applied to obtain the optimal incentive allocation. However, the large amount
of biased data in the training set, generated by the previous biased allocation
policy, causes a biased …

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