提出可直接优化利润的干扰感知提升建模方法。
Direct Profit Estimation Using Uplift Modeling under Clustered Network Interference
- 用可微分的AddIPW估计器替代传统方法，处理网络干扰
- 在模拟中显著优于忽略干扰的方法，干扰越强优势越明显
- 适合需要精准利润导向推荐的电商与广告场景
提升建模是推荐系统促销优化的关键技术，但传统方法通常忽略干扰现象——对一个商品的干预会影响其他商品的转化结果。这种对稳定单元处理值假设（SUTVA）的违背导致真实市场中的策略表现不佳。尽管近期出现如加法逆倾向加权（AddIPW）等干扰感知估计器，但尚未被引入提升建模领域，且基于这些估计器的策略优化也未充分研究。本文提出一种实用方法：将AddIPW作为可微分学习目标，适用于梯度优化；并结合成熟的响应变换技术，直接优化经济指标如增量利润。仿真结果显示，该方法在干扰增强时显著优于忽略干扰的方法。此外，在框架内调整以利润为核心的提升策略，能更有效识别高影响力干预措施，为实现更高收益的个性化激励提供可行路径。
原文摘要 · Abstract (English)
Uplift modeling is a key technique for promotion optimization in recommender systems, but standard methods typically fail to account for interference, where treating one item affects the outcomes of others. This violation of the Stable Unit Treatment Value Assumption (SUTVA) leads to suboptimal policies in real-world marketplaces. Recent developments in interference-aware estimators such as Additive Inverse Propensity Weighting (AddIPW) have not found their way into the uplift modeling literature yet, and optimising policies using these estimators is not well-established. This paper proposes a practical methodology to bridge this gap. We use the AddIPW estimator as a differentiable learning objective suitable for gradient-based optimization. We demonstrate how this framework can be integrated with proven response transformation techniques to directly optimize for economic outcomes like incremental profit. Through simulations, we show that our approach significantly outperforms interference-naive methods, especially as interference effects grow. Furthermore, we find that adapting profit-centric uplift strategies within our framework can yield superior performance in identifying the highest-impact interventions, offering a practical path toward more profitable incentive personalization.
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