Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

TL;DR

This paper introduces an adversarial learning approach to correct bias in incentive allocation models for mobile payment marketing, significantly improving policy performance using both biased and unbiased data.

Contribution

The paper proposes a novel bias correction adversarial network that leverages small unbiased data sets to improve incentive allocation models trained on biased data.

Findings

Outperforms state-of-the-art methods in experiments

Improves allocation policy performance in real-world campaigns

Effectively reduces bias in response estimation models

Abstract

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 estimation. This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy. To overcome this obstacle, we propose a bias correction adversarial network. Our method leverages the small set of unbiased data obtained under a full-randomized allocation policy to train an unbiased model and then uses it to reduce the bias with adversarial learning. Offline and online experimental results demonstrate that our method outperforms state-of-the-art approaches and significantly improves the performance of the resulting allocation policy in a real-world marketing campaign.