  Representation learning has been a critical topic in machine learning. In
Click-through Rate Prediction, most features are represented as embedding
vectors and learned simultaneously with other parameters in the model. With the
development of CTR models, feature representation learning has become a
trending topic and has been extensively studied by both industrial and academic
researchers in recent years. This survey aims at summarizing the feature
representation learning in a broader picture and pave the way for future
research. To achieve such a goal, we first present a taxonomy of current
research methods on feature representation learning following two main issues:
(i) which feature to represent and (ii) how to represent these features. Then
we give a detailed description of each method regarding these two issues.
Finally, the review concludes with a discussion on the future directions of
this field.
