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.

翻译：点击率(CTR)预测对于建议和在线广告系统至关重要。 现有的方法通常模拟用户行为,而忽视影响用户做出点击决定的信息环境,例如点击页面和预选候选人,这些候选人告知用户兴趣的推论,导致业绩低于最佳水平。 在本文中,我们提议建立一个决策-决策背景互动网络(DCIN),它部署一个精心设计的“环境互动股(CIU)”,以学习决策背景,从而有利于CTR预测。此外,拟议的“适应性利益集合股”正在探讨不同决策背景来源之间的关系,以进一步改进CTR预测。在公共和工业数据集的实验中,DCIN大大超越了最新方法。值得注意的是,该模型已经改进了CTR+2.9%/CPM+2.1%/GMV+1.5%用于在线A/B测试,并为Mituan Waimai广告系统的主要交通提供了服务。