  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.
