Decision-Making Context Interaction Network for Click-Through Rate Prediction
This addresses suboptimal performance in recommendation and advertising systems for platforms like Meituan Waimai, though it appears incremental by focusing on context modeling.
The paper tackles the problem of click-through rate prediction by modeling decision-making contexts like click pages and pre-ranking candidates, which are often ignored, resulting in improved performance with gains of CTR+2.9%, CPM+2.1%, and GMV+1.5% in online A/B testing.
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.