Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus on user behavior modeling to improve CTR prediction performance. However, most of those methods only model users' positive interests from users' click items while ignoring the context information, which is the display items around the clicks, resulting in inferior performance. In this paper, we highlight the importance of context information on user behavior modeling and propose a novel model named Deep Context Interest Network (DCIN), which integrally models the click and its display context to learn users' context-aware interests. DCIN consists of three key modules: 1) Position-aware Context Aggregation Module (PCAM), which performs aggregation of display items with an attention mechanism; 2) Feedback-Context Fusion Module (FCFM), which fuses the representation of clicks and display contexts through non-linear feature interaction; 3) Interest Matching Module (IMM), which activates interests related with the target item. Moreover, we provide our hands-on solution to implement our DCIN model on large-scale industrial systems. The significant improvements in both offline and online evaluations demonstrate the superiority of our proposed DCIN method. Notably, DCIN has been deployed on our online advertising system serving the main traffic, which brings 1.5% CTR and 1.5% RPM lift.

翻译：点击率（CTR）预测，即估计用户点击某个项目的概率，在在线广告等工业应用中至关重要。许多研究聚焦于用户行为建模以提升CTR预测性能。然而，这些方法大多仅从用户的点击项目中建模用户的正面兴趣，而忽略了上下文信息（即点击周围的展示项目），导致性能不佳。本文强调了上下文信息对用户行为建模的重要性，并提出了一种名为深度上下文兴趣网络（DCIN）的新模型，该模型综合建模点击及其展示上下文以学习用户的上下文感知兴趣。DCIN包含三个关键模块：1）位置感知上下文聚合模块（PCAM），通过注意力机制聚合展示项目；2）反馈-上下文融合模块（FCFM），通过非线性特征交互融合点击和展示上下文的表示；3）兴趣匹配模块（IMM），激活与目标项目相关的兴趣。此外，我们提供了在大型工业系统中实现DCIN模型的实践方案。离线与在线评估的显著改进证明了我们提出的DCIN方法的优越性。值得注意的是，DCIN已部署在我们的在线广告系统中，服务于主要流量，实现了1.5%的CTR提升和1.5%的RPM提升。