Deep Context Interest Network for Click-Through Rate Prediction

TL;DR

This paper introduces DCIN, a novel deep learning model that incorporates display context into user interest modeling for CTR prediction, leading to significant offline and online performance improvements in industrial advertising systems.

Contribution

The paper proposes a new model, DCIN, that effectively integrates display context with user click data for improved CTR prediction, and demonstrates its deployment in a real-world system.

Findings

DCIN achieves 1.5% CTR lift in online advertising.

DCIN outperforms existing models in offline evaluations.

Deployment of DCIN improves overall advertising system performance.

Abstract

Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus on user behavior modeling to improve CTR prediction performance. However, most of those methods only model users' positive interests from users' click items while ignoring the context information, which is the display items around the clicks, resulting in inferior performance. In this paper, we highlight the importance of context information on user behavior modeling and propose a novel model named Deep Context Interest Network (DCIN), which integrally models the click and its display context to learn users' context-aware interests. DCIN consists of three key modules: 1) Position-aware Context Aggregation Module (PCAM), which performs aggregation of display items with an attention mechanism; 2) Feedback-Context Fusion Module (FCFM), which fuses the representation of clicks and display contexts through non-linear feature interaction; 3) Interest Matching Module (IMM), which activates interests related with the target item. Moreover, we provide our hands-on solution to implement our DCIN model on large-scale industrial systems. The significant improvements in both offline and online evaluations demonstrate the superiority of our proposed DCIN method. Notably, DCIN has been deployed on our online advertising system serving the main traffic, which brings 1.5% CTR and 1.5% RPM lift.