Click-through rate prediction is an important method for online advertising and marketing evaluations. However, for environmental reasons, there is a scarcity and imbalance in the advertising data available. We found that a feature transfer can be applied in a transfer learning method to obtain potential connections from less relevant advertisement data. Considering the complexity and diverse features of advertisement data, a feature transfer cannot allow researchers to discover the relationships among such features within the advertisement data. Therefore, we propose a click-through rate method based on a multi-view feature transfer (MFT). MFT divides the data into common and selected features during the data pretreatment process. It then combines the important feature vectors obtained using a Laplacian matrix with the common features obtained during the pretreatment process to form groups of views. Therefore, combining feature transfer matrix with mutli-view clustering is an innovation of the CTR data prediction process. In our experiments, the MFT model achieved good results. Experiments on a large number of datasets of different sizes and the application of three evaluation indicators show that the MFT method delivers excellent prediction results using the transfer relationships among the characteristics of an advertising dataset, and its performance is better than that of many other advertising click-through rate prediction methods.

点击率预测是在线广告和营销评估的重要方法。但是，由于环境原因，可用的广告数据稀缺和不平衡。我们发现，可以将特征转移应用于转移学习方法中，以从不太相关的广告数据中获得潜在的联系。考虑到广告数据的复杂性和多样的特征，特征转移不能使研究人员发现广告数据中这些特征之间的关系。因此，我们提出了一种基于多视图特征转移（MFT）的点击率方法。MFT在数据预处理过程中将数据分为常见特征和选定特征。然后，它将使用拉普拉斯矩阵获得的重要特征向量与预处理过程中获得的共同特征相结合，以形成视图组。因此，将特征传递矩阵与多视图聚类相结合是CTR数据预测过程的一项创新。在我们的实验中，MFT模型取得了良好的效果。在大量不同大小的数据集上的实验以及三个评估指标的应用表明，MFT方法利用广告数据集特征之间的传递关系提供了出色的预测结果，其性能优于许多其他广告点击通过率预测方法。将特征传递矩阵与多视图聚类相结合是CTR数据预测过程的一项创新。在我们的实验中，MFT模型取得了良好的效果。在大量不同大小的数据集上的实验以及三个评估指标的应用表明，MFT方法利用广告数据集特征之间的传递关系提供了出色的预测结果，其性能优于许多其他广告点击通过率预测方法。将特征传递矩阵与多视图聚类相结合是CTR数据预测过程的一项创新。在我们的实验中，MFT模型取得了良好的效果。在大量不同大小的数据集上的实验以及三个评估指标的应用表明，MFT方法利用广告数据集特征之间的传递关系提供了出色的预测结果，其性能优于许多其他广告点击通过率预测方法。