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.

翻译：表示学习一直是机器学习中的关键课题。在点击率预测中，大多数特征被表示为嵌入向量，并与模型中的其他参数共同学习。随着点击率预测模型的发展，特征表示学习已成为一个热门话题，近年来受到工业界和学术界研究者的广泛关注。本综述旨在从更广阔的视角总结特征表示学习，并为未来研究铺平道路。为实现这一目标，我们首先提出当前特征表示学习研究方法的一个分类体系，该分类围绕两个核心问题：（i）表示哪些特征；（ii）如何表示这些特征。随后，我们针对这两个问题详细描述每种方法。最后，本综述对该领域的未来发展方向进行了讨论。