Dynamic Group Parameter Modeling for Click-Through-Rate Prediction
Xuan Ma, Jian Wang, Zhiyuan Chen, Zehua Zhang, Jie He, Changping Peng, Zhangang Lin, Jingping Shao · 2023
It is noted that Click-Through-Rate(CTR) prediction plays an important part in recommendation systems and online advertising. Over the past few years, numerous studies have been conducted to improve the accuracy of CTR prediction by exploring data inherent patterns. These studies indicate that training CTR models with group-specific parameters on divided data groups can lead to significant improvements. However, most works generally divide groups manually with some prior knowledge, and such a fixed group division method may hinder the expression of user common interests. To address this limitation, we propose a novel group parameter modeling method, where the user group division and group parameter learning processes are completed in an automatic and dynamic way. Our method employs a three-stage approach, consisting of group information selection, group representation learning, and group parameter generation, which allows the efficient expression of user common interests. We conduct experiments on both public datasets and industrial datasets, and the experimental results demonstrate the effectiveness of our method. We have also deployed the model in an online advertising system and observed significant improvements in both CTR and Revenue Per Mille (RPM).