Decision-Making Context Interaction Network for Click-Through Rate Prediction

TL;DR

This paper introduces the Decision-Making Context Interaction Network (DCIN), a novel model that incorporates decision-making contexts and their relationships to enhance click-through rate prediction, outperforming existing methods in accuracy and online performance.

Contribution

The paper proposes the DCIN model with the CIU and AIAU modules, capturing decision-making contexts and their relationships, which improves CTR prediction accuracy over state-of-the-art methods.

Findings

DCIN outperforms existing models on public and industrial datasets.

Online A/B testing shows CTR+2.9%, CPM+2.1%, GMV+1.5% improvements.

DCIN successfully served the main traffic of Meituan Waimai advertising system.

Abstract

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.