Uplift modeling is a technique used to predict the effect of a treatment (e.g., discounts) on an individual's response. Although several methods have been proposed for multi-valued treatment, they are extended from binary treatment methods. There are still some limitations. Firstly, existing methods calculate uplift based on predicted responses, which may not guarantee a consistent uplift distribution between treatment and control groups. Moreover, this may cause cumulative errors for multi-valued treatment. Secondly, the model parameters become numerous with many prediction heads, leading to reduced efficiency. To address these issues, we propose a novel \underline{M}ulti-gate \underline{M}ixture-of-Experts based \underline{M}ulti-valued \underline{T}reatment \underline{N}etwork (M$^3$TN). M$^3$TN consists of two components: 1) a feature representation module with Multi-gate Mixture-of-Experts to improve the efficiency; 2) a reparameterization module by modeling uplift explicitly to improve the effectiveness. We also conduct extensive experiments to demonstrate the effectiveness and efficiency of our M$^3$TN.

翻译：提升建模是一种用于预测处理（如折扣）对个体响应影响的技术。尽管已有多种针对多值处理的方法被提出，但它们多由二元处理方法扩展而来，仍存在一些局限性。首先，现有方法基于预测响应计算提升量，可能无法确保处理组与对照组之间的提升分布一致性。此外，这可能导致多值处理的累积误差。其次，随着预测头数量的增加，模型参数变得冗余，从而降低效率。为解决这些问题，我们提出了一种新颖的基于多门混合专家的多值处理网络（M$^3$TN）。M$^3$TN包含两个组件：1）采用多门混合专家的特征表示模块，以提高效率；2）通过显式建模提升量的重参数化模块，以提升有效性。我们还进行了大量实验，以证明M$^3$TN的有效性和效率。