Cross features play an important role in click-through rate (CTR) prediction. Most of the existing methods adopt a DNN-based model to capture the cross features in an implicit manner. These implicit methods may lead to a sub-optimized performance due to the limitation in explicit semantic modeling. Although traditional statistical explicit semantic cross features can address the problem in these implicit methods, it still suffers from some challenges, including lack of generalization and expensive memory cost. Few works focus on tackling these challenges. In this paper, we take the first step in learning the explicit semantic cross features and propose Pre-trained Cross Feature learning Graph Neural Networks (PCF-GNN), a GNN based pre-trained model aiming at generating cross features in an explicit fashion. Extensive experiments are conducted on both public and industrial datasets, where PCF-GNN shows competence in both performance and memory-efficiency in various tasks.

在CTR预测中，本文提出了一种基于预训练的图神经网络(PCF-GNN)的显式语义交叉特征学习模型，解决了传统方法的泛化和内存成本的问题，并在公共和工业数据集上展示出在不同任务中的卓越表现和内存效率。

使用预训练图神经网络进行显式语义交叉特征学习以进行CTR预测