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：提升建模在在线营销中至关重要，其旨在通过预测个体处理效应来准确衡量不同策略（如优惠券或折扣）对不同用户的影响。在电子商务场景中，用户行为遵循一个明确的序列链，包括曝光、点击和转化。营销策略在该链的每个阶段产生不同的提升效应，影响诸如点击率和转化率等指标。尽管其实用，现有研究忽略了特定处理下所有阶段间的跨任务影响，并且未能充分利用处理信息，这可能给后续营销决策引入显著偏差。我们将这两个问题识别为链式偏差问题与处理非自适应问题。本文提出了基于上下文增强学习的全链路提升方法，旨在解决这些问题。ECUP包含两个主要组件：1) 全链路增强网络，其利用用户行为模式在整个链式空间中估计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，从而捕捉处理对不同任务的影响；以及2) 处理增强网络，其通过比特级特征交互促进细粒度的处理建模，从而实现自适应特征调整。在公开和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，为数百万日活跃用户提供服务，相关数据集已发布以供未来研究。