Online platforms often incentivize consumers to improve user engagement and platform revenue. Since different consumers might respond differently to incentives, individual-level budget allocation is an essential task in marketing campaigns. Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution. Since the objectives of these two stages might not be perfectly aligned, such a two-stage paradigm could hurt the overall marketing effectiveness. In this paper, we propose a novel end-to-end framework to directly optimize the business goal under budget constraints. Our core idea is to construct a regularizer to represent the marketing goal and optimize it efficiently using gradient estimation techniques. As such, the obtained models can learn to maximize the marketing goal directly and precisely. We extensively evaluate our proposed method in both offline and online experiments, and experimental results demonstrate that our method outperforms current state-of-the-art methods. Our proposed method is currently deployed to allocate marketing budgets for hundreds of millions of users on a short video platform and achieves significant business goal improvements. Our code will be publicly available.

翻译：在线平台常通过激励手段提升用户参与度与平台收益。由于不同用户对激励措施的响应存在差异，个体级预算分配已成为营销活动中的关键任务。该领域最新进展通常采用两阶段范式解决预算分配问题：第一阶段利用因果推断算法估计个体处理效应，第二阶段通过整数规划技术求解最优预算分配方案。然而，由于两个阶段的优化目标可能存在不一致性，这种两阶段范式可能损害整体营销效果。本文提出一种新颖的端到端框架，能够在预算约束下直接优化业务目标。核心思想是构建表征营销目标的正则化项，并采用梯度估计技术实现高效优化，使模型能够直接且精准地学习最大化营销目标。我们在线上与线下实验中对该方法进行了全面评估，结果表明其性能优于当前最先进的算法。该方法已部署于某短视频平台，为亿级用户分配营销预算，并显著提升了业务目标指标。相关代码将公开提供。