点击通过率估计深度学习 (Deep Learning for Click-Through Rate Estimation)

Click-through rate (CTR) estimation plays as a core function module in various personalized online services, including online advertising, recommender systems, and web search etc. From 2015, the success of deep learning started to benefit CTR estimation performance and now deep CTR models have been widely applied in many industrial platforms. In this survey, we provide a comprehensive review of deep learning models for CTR estimation tasks. First, we take a review of the transfer from shallow to deep CTR models and explain why going deep is a necessary trend of development. Second, we concentrate on explicit feature interaction learning modules of deep CTR models. Then, as an important perspective on large platforms with abundant user histories, deep behavior models are discussed. Moreover, the recently emerged automated methods for deep CTR architecture design are presented. Finally, we summarize the survey and discuss the future prospects of this field.

翻译：从2015年起,深层次学习的成功开始有利于CTR估计业绩,现在深入的CTR模型已在许多工业平台中广泛应用。在本次调查中,我们全面审查了CTR估算任务的深层次学习模型。首先,我们审查了从浅层到深层CTR模型的转移,并解释了深层的转移为什么是一个必要的发展趋势。第二,我们集中关注深层CTR模型的清晰特征互动学习模块。然后,作为拥有大量用户历史的大型平台的重要视角,我们讨论了深层行为模型。此外,还介绍了最近出现的深层CTR结构设计自动化方法。最后,我们总结了调查,并讨论了该领域的未来前景。