  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.
