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）预测在推荐系统和在线广告系统中至关重要。现有方法通常对用户行为进行建模，而忽略了影响用户做出点击决策的信息性上下文，例如点击页面和预排序候选集，这些内容能够推断用户兴趣，从而导致性能欠佳。本文提出一种决策上下文交互网络（DCIN），该网络通过精心设计的上下文交互单元（CIU）来学习决策上下文，从而提升CTR预测效果。此外，所提出的自适应兴趣聚合单元（AIAU）探索了不同决策上下文源之间的关系，进一步改善了CTR预测。在公开数据集和工业数据集上的实验表明，DCIN显著优于现有最先进方法。值得注意的是，该模型在在线A/B测试中实现了CTR+2.9%/CPM+2.1%/GMV+1.5%的提升，并服务于美团外卖广告系统的主要流量。