  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.
