Deep Interest Evolution Network for Click-Through Rate Prediction

TL;DR

This paper introduces DIEN, a novel deep learning model that captures dynamic user interest evolution for improved click-through rate prediction, significantly outperforming existing methods and deployed successfully in Taobao's advertising system.

Contribution

The paper proposes DIEN, a new model that explicitly captures interest evolution over time using interest extractor and evolving layers with attention mechanisms.

Findings

DIEN outperforms state-of-the-art CTR prediction models.

DIEN achieves a 20.7% CTR improvement in Taobao's system.

Interest evolution modeling enhances prediction accuracy.

Abstract

Click-through rate~(CTR) prediction, whose goal is to estimate the probability of the user clicks, has become one of the core tasks in advertising systems. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external environment and the internal cognition, user interest evolves over time dynamically. There are several CTR prediction methods for interest modeling, while most of them regard the representation of behavior as the interest directly, and lack specially modeling for latent interest behind the concrete behavior. Moreover, few work consider the changing trend of interest. In this paper, we propose a novel model, named Deep Interest Evolution Network~(DIEN), for CTR prediction. Specifically, we design interest extractor layer to capture temporal interests from history behavior sequence. At this layer, we introduce an auxiliary loss to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, we propose interest evolving layer to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution. In the experiments on both public and industrial datasets, DIEN significantly outperforms the state-of-the-art solutions. Notably, DIEN has been deployed in the display advertisement system of Taobao, and obtained 20.7\% improvement on CTR.