Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individual Treatment Effect (ITE). In an e-commerce setting, user behavior follows a defined sequential chain, including impression, click, and conversion. Marketing strategies exert varied uplift effects at each stage within this chain, impacting metrics like click-through and conversion rate. Despite its utility, existing research has neglected to consider the inter-task across all stages impacts within a specific treatment and has insufficiently utilized the treatment information, potentially introducing substantial bias into subsequent marketing decisions. We identify these two issues as the chain-bias problem and the treatment-unadaptive problem. This paper introduces the Entire Chain UPlift method with context-enhanced learning (ECUP), devised to tackle these issues. ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment. Extensive experiments on public and industrial datasets validate ECUPs effectiveness. Moreover, ECUP has been deployed on the Meituan food delivery platform, serving millions of daily active users, with the related dataset released for future research.

翻译：提升建模是在线营销中的关键技术，旨在通过预测个体处理效应（ITE）准确衡量不同策略（如优惠券或折扣）对各类用户的影响。在电商场景中，用户行为遵循明确的顺序链条，包括曝光、点击和转化。营销策略在此链条的每个阶段会产生不同的提升效应，影响点击率、转化率等指标。尽管现有研究具有一定应用价值，但未能考虑特定处理下所有阶段任务间的相互影响，且对处理信息的利用不充分，这可能导致后续营销决策产生显著偏差。我们将这两个问题分别定义为链条偏差问题与处理不适应性问题。本文提出了一种基于上下文增强学习的整个链条提升方法（ECUP），旨在解决上述问题。ECUP包含两个核心组件：1）整个链条增强网络——利用用户行为模式估算整个链条空间中的ITE，建模处理对每个任务的不同影响，并整合任务先验信息以增强所有阶段的上下文感知能力，捕捉处理对不同任务的影响；2）处理增强网络——通过比特级特征交互实现细粒度处理建模，从而支持自适应特征调整。在公开数据集与工业数据集上的大量实验验证了ECUP的有效性。此外，ECUP已在美团外卖平台部署，服务数百万日活跃用户，并发布了相关数据集以供未来研究使用。