Ensemble Knowledge Distillation for CTR Prediction

TL;DR

This paper introduces an ensemble knowledge distillation approach for CTR prediction that simplifies models while improving accuracy, using novel techniques like teacher gating and early stopping, validated through extensive experiments.

Contribution

It proposes a new ensemble knowledge distillation method with techniques like teacher gating and early stopping to enhance CTR prediction accuracy and efficiency.

Findings

Ensemble KD improves CTR prediction accuracy over individual models.

The proposed methods outperform 12 existing models in experiments.

Online and offline tests confirm the effectiveness of the approach.

Abstract

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. However, current research focuses primarily on building complex network architectures to better capture sophisticated feature interactions and dynamic user behaviors. The increased model complexity may slow down online inference and hinder its adoption in real-time applications. Instead, our work targets at a new model training strategy based on knowledge distillation (KD). KD is a teacher-student learning framework to transfer knowledge learned from a teacher model to a student model. The KD strategy not only allows us to simplify the student model as a vanilla DNN model but also achieves significant accuracy improvements over the state-of-the-art teacher models. The benefits thus motivate us to further explore the use of a powerful ensemble of teachers for more accurate student model training. We also propose some novel techniques to facilitate ensembled CTR prediction, including teacher gating and early stopping by distillation loss. We conduct comprehensive experiments against 12 existing models and across three industrial datasets. Both offline and online A/B testing results show the effectiveness of our KD-based training strategy.