Online marketing is critical for many industrial platforms and business
applications, aiming to increase user engagement and platform revenue by
identifying corresponding delivery-sensitive groups for specific incentives,
such as coupons and bonuses. As the scale and complexity of features in
industrial scenarios increase, deep uplift modeling (DUM) as a promising
technique has attracted increased research from academia and industry,
resulting in various predictive models. However, current DUM still lacks some
standardized benchmarks and unified evaluation protocols, which limit the
reproducibility of experimental results in existing studies and the practical
value and potential impact in this direction. In this paper, we provide an open
benchmark for DUM and present comparison results of existing models in a
reproducible and uniform manner. To this end, we conduct extensive experiments
on two representative industrial datasets with different preprocessing settings
to re-evaluate 13 existing models. Surprisingly, our experimental results show
that the most recent work differs less than expected from traditional work in
many cases. In addition, our experiments also reveal the limitations of DUM in
generalization, especially for different preprocessing and test distributions.
Our benchmarking work allows researchers to evaluate the performance of new
models quickly but also reasonably demonstrates fair comparison results with
existing models. It also gives practitioners valuable insights into often
overlooked considerations when deploying DUM. We will make this benchmarking
library, evaluation protocol, and experimental setup available on GitHub.

在线营销是许多工业平台和商业应用的关键，本文提出了一个用于深层提升建模技术的开放基准，并以可重复和统一的方式比较了现有模型的结果。结果显示，最新的工作在许多情况下与传统工作相比差异较小，并揭示了深层提升建模在泛化方面的局限性，特别是在不同的预处理和测试分布情况下。

在线营销中的深度提升建模基准测试

Benchmarking for Deep Uplift Modeling in Online Marketing

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.

提出了一种新的强化鲁棒性的提升建模框架 RUAD，通过两个定制模块，即联合多标签建模的特征选择模块和利用对抗训练和软插值操作的对抗性特征去敏模块，更有效地缓解提升模型的特征敏感性，从而提高在线营销中的效果和鲁棒性。