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.

通过模型命名为深度上下文兴趣网络 (DCIN)，本文突出了上下文信息对用户行为建模的重要性，并提出了一种可完整建模点击及其显示上下文以了解用户上下文感知兴趣的新模型。通过离线和在线评估，显著改善了性能，证明了所提出的 DCIN 方法的优越性。特别值得注意的是，已在我们的在线广告系统上部署了 DCIN，为主要流量带来了 1.5% 的 CTR 和 1.5% 的 RPM 提升。