标题： 显式特征交互感知的在线营销提升网络 显示英文标题

标题： Explicit Feature Interaction-aware Uplift Network for Online Marketing

摘要： 作为在线营销中的关键组件，提升建模旨在准确捕捉不同处理对不同用户的影响程度，例如优惠券或折扣，也称为个体处理效应（ITE）的估计。 在实际的业务场景中，处理选项可能众多且复杂，并且不同处理之间可能存在相关性。 此外，每个营销实例也可能拥有丰富的用户和上下文特征。 然而，现有方法在充分利用处理信息和挖掘对特定处理敏感的特征方面仍存在不足。 在本文中，我们提出了一种显式特征交互感知提升网络（EFIN）来解决这两个问题。 我们的EFIN包含四个定制模块：1）特征编码模块不仅编码用户和上下文特征，还编码处理特征；2）自交互模块旨在准确建模用户在不考虑处理特征情况下的自然反应；3）处理感知交互模块通过处理特征与其他特征之间的交互，准确建模特定处理对用户的影响程度，即ITE；以及4）干预约束模块用于平衡对照组和处理组之间的ITE分布，从而使模型在从非随机干预营销场景中收集的数据上仍能实现准确的提升排序。 我们在两个公开数据集和一个产品数据集上进行了广泛的实验，以验证EFIN的有效性。 此外，我们的EFIN已在一家大型在线金融平台的信用卡账单支付场景中部署，并取得了显著改进。 显示英文摘要

摘要： As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an actual business scenario, the options for treatment may be numerous and complex, and there may be correlations between different treatments. In addition, each marketing instance may also have rich user and contextual features. However, existing methods still fall short in both fully exploiting treatment information and mining features that are sensitive to a particular treatment. In this paper, we propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i.e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario. We conduct extensive experiments on two public datasets and one product dataset to verify the effectiveness of our EFIN. In addition, our EFIN has been deployed in a credit card bill payment scenario of a large online financial platform with a significant improvement.