  Click-through rate (CTR) prediction is crucial in recommendation and online
advertising systems. Existing methods usually model user behaviors, while
ignoring the informative context which influences the user to make a click
decision, e.g., click pages and pre-ranking candidates that inform inferences
about user interests, leading to suboptimal performance. In this paper, we
propose a Decision-Making Context Interaction Network (DCIN), which deploys a
carefully designed Context Interaction Unit (CIU) to learn decision-making
contexts and thus benefits CTR prediction. In addition, the relationship
between different decision-making context sources is explored by the proposed
Adaptive Interest Aggregation Unit (AIAU) to improve CTR prediction further. In
the experiments on public and industrial datasets, DCIN significantly
outperforms the state-of-the-art methods. Notably, the model has obtained the
improvement of CTR+2.9%/CPM+2.1%/GMV+1.5% for online A/B testing and served the
main traffic of Meituan Waimai advertising system.
