完整链式提升建模与上下文增强学习用于智能营销
Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
AI总结：
本文提出ECUP方法，通过上下文增强学习解决链偏和治疗不适应问题，提升营销策略效果评估的准确性。
摘要：
提升建模在在线营销中至关重要，旨在通过预测个体治疗效应（ITE）来准确衡量各种策略（如优惠券或折扣）对不同用户的影响。在电子商务环境中，用户行为遵循定义好的连续链，包括印象、点击和转化。营销策略在该链的每个阶段会产生不同的提升效应，影响点击率和转化率等指标。尽管其有用性，现有研究忽略了特定治疗过程中所有阶段之间的任务影响，并且未能充分利用治疗信息，这可能会对后续营销决策引入显著的偏见。我们识别这两个问题为链偏问题和治疗不适应问题。本文介绍了一种完整的链提升方法，结合上下文增强学习（ECUP），旨在解决这些问题。ECUP由两个主要组成部分：1）完整的链增强网络，利用用户行为模式在整个链空间中估计ITE，建模治疗对每个任务的各种影响，并整合任务先验信息以增强所有阶段的上下文意识，捕捉治疗对不同任务的影响；2）治疗增强网络，通过位级特征交互实现细粒度治疗建模，从而实现适应性特征调整。在公开和工业数据集上的广泛实验验证了ECUP的有效性。此外，ECUP已部署在美团外卖平台，服务数百万日活跃用户，相关数据集已发布供未来研究使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
