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arXiv · 2302.02241

Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives

Fuyuan Lyu·Xing Tang·Dugang Liu·Haolun Wu·Chen Ma·Xiuqiang He·Xue Liu
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Abstract

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.

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Fuyuan Lyu, Xing Tang, Dugang Liu, Haolun Wu, Chen Ma, Xiuqiang He, Xue Liu. 2023-02-04. Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives. https://arxiv.org/abs/2302.02241

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