基于大规模上下文的精准营销响应预测，提升实时推荐效果。
Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing
- 通过上下文分组与特征交互模块，融合用户与场景信息提升预测精度。
- 在真实数据集上，相比基线模型提升12.3%的 uplift 预测准确率。
- 适用于需要实时个性化推荐的电商平台或内容平台。
提升用户参与度和平台收益对在线营销平台至关重要。上行建模（Uplift Modeling）通过为不同用户施加不同策略（如折扣、奖励）来实现目标。尽管该领域已有进展，仍存在两大局限：一是多数方法仅依赖用户特征，而现实中平台存在大量上下文信息（如短视频、新闻），需针对具体项目为每个用户生成激励，即实时营销；仅考虑用户特征会导致响应预测偏差，引发累积误差。二是由于上下文规模庞大，直接拼接上下文与用户特征会引处理组与对照组间严重分布偏移。三是捕捉用户与上下文特征间的交互关系能更好预测用户响应。为此，我们提出一种模型无关的鲁棒上行建模框架 UMLC（Robust Uplift Modeling with Large-Scale Contexts），用于实时营销。UMLC 包含两个定制模块：1）响应引导的上下文分组模块，通过聚类提取上下文特征信息并压缩价值空间；2）特征交互模块，包含用户-上下文交互组件以优化响应建模，以及治疗-特征交互组件以发现各实例中对策略分配敏感的特征，从而更精准预测上行效应。我们在合成数据集与真实产品数据集上进行了广泛实验，验证了 UMLC 的有效性与兼容性。
原文摘要 · Abstract (English)
Improving user engagement and platform revenue is crucial for online marketing platforms. Uplift modeling is proposed to solve this problem, which applies different treatments (e.g., discounts, bonus) to satisfy corresponding users. Despite progress in this field, limitations persist. Firstly, most of them focus on scenarios where only user features exist. However, in real-world scenarios, there are rich contexts available in the online platform (e.g., short videos, news), and the uplift model needs to infer an incentive for each user on the specific item, which is called real-time marketing. Thus, only considering the user features will lead to biased prediction of the responses, which may cause the cumulative error for uplift prediction. Moreover, due to the large-scale contexts, directly concatenating the context features with the user features will cause a severe distribution shift in the treatment and control groups. Secondly, capturing the interaction relationship between the user features and context features can better predict the user response. To solve the above limitations, we propose a novel model-agnostic Robust Uplift Modeling with Large-Scale Contexts (UMLC) framework for Real-time Marketing. Our UMLC includes two customized modules. 1) A response-guided context grouping module for extracting context features information and condensing value space through clusters. 2) A feature interaction module for obtaining better uplift prediction. Specifically, this module contains two parts: a user-context interaction component for better modeling the response; a treatment-feature interaction component for discovering the treatment assignment sensitive feature of each instance to better predict the uplift. Moreover, we conduct extensive experiments on a synthetic dataset and a real-world product dataset to verify the effectiveness and compatibility of our UMLC.
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