关键词: 点击率预测, 机器学习, 卷积神经网络, 长短期记忆

Abstract: Click through rate prediction is one of the core algorithms for computational advertising. The traditional shallow model does not fully consider the non-linear relationship between data, and the artificial feature extraction method is time-consuming and laborious. Aiming at these problems, this paper presents a click through rate prediction model based on CNN（Convolutional Neural Networks）-LSTM（Long Short Term Memory） hybrid network. This model uses CNN to extract high-impact features, and predicts and classifies them using the sequence of LSTM. The experimental results show that compared with the shallow model or single neural network model, the hybrid neural network model based on CNN-LSTM can effectively improve the accuracy of advertising click through rate prediction.

Key words: Click Through Rate（CTR） prediction, machine learning, Convolutional Neural Networks（CNN）, Long Short Term Memory（LSTM）