Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization

• URL: http://arxiv.org/abs/2310.04693v3
• Date: Fri, 29 Dec 2023 09:54:36 GMT
• Title: Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
• Authors: Zexu Sun, Bowei He, Ming Ma, Jiakai Tang, Yuchen Wang, Chen Ma, Dugang Liu
• Abstract summary: We propose a novel robustness-enhanced uplift modeling framework with adversarial feature desensitization (RUAD) Our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules. We conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of our RUAD in online marketing.
• Score: 11.404726761497798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uplift modeling has shown very promising results in online marketing. However, most existing works are prone to the robustness challenge in some practical applications. In this paper, we first present a possible explanation for the above phenomenon. We verify that there is a feature sensitivity problem in online marketing using different real-world datasets, where the perturbation of some key features will seriously affect the performance of the uplift model and even cause the opposite trend. To solve the above problem, we propose a novel robustness-enhanced uplift modeling framework with adversarial feature desensitization (RUAD). Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features. Finally, we conduct extensive experiments on a public dataset and a real product dataset to verify the effectiveness of our RUAD in online marketing. In addition, we also demonstrate the robustness of our RUAD to the feature sensitivity, as well as the compatibility with different uplift models.

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