针对互联网广告数据具有高维稀疏性的特点, 在现有的点击率(Click-Through Rate, CTR)预测问题的相关理论和技术基础上, 给出了一种基于梯度提升决策树(Gradient Boosting Decision Tree, GBDT)的卷积神经网络(Convolutional Neural Networks, CNN)在线广告特征提取模型(CNN Based on GBDT, CNN+). CNN+模型不仅能从原始数据中提取出深度高阶特征, 还能解决卷积神经网络在稀疏、高维特征中提取特征困难的问题. 在真实数据集上的实验结果表明, 与主成分分析(Principal Component Analysis, PCA)和梯度提升决策树这两种特征提取方法相比, CNN+模型提取的特征更加有效.
This paper proposes an online advertising feature extraction model of CNN (Convolutional Neural Networks) based on GBDT (Gradient Boosting Decision Tree) aimed at solving challenges with high-dimensional sparseness in Internet advertising data based on existing theories and technologies for click-through rate (CRT) prediction. The proposed model, CNN+, is able to extract deep, high-order features from raw data and solve the issues that convolutional neural networks face in extracting sparse and high-dimensional features. Experimental results on real datasets show that the features extracted by the CNN+ model are more effective than two other feature extraction methods studied, namely principal component analysis (PCA) and GBDT.
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中国综合性科技类核心期刊(北大核心)