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）

摘要： 点击率预测是计算广告学的核心算法之一。传统浅层模型没有充分考虑到数据之间存在的非线性关系，且使用人工特征提取方法费时费力。针对这些问题，提出了基于卷积（Convolutional Neural Networks）-LSTM（Long Short Term Memory）混合神经网络的广告点击率预测模型。该模型使用卷积神经网络提取高影响力特征，并通过LSTM神经网络的时序性进行预测分类。实验结果证明：与浅层模型或单一结构的神经网络模型相比，基于卷积-LSTM的混合神经网络模型能有效提高广告点击事件的预测准确率。

关键词: 点击率预测, 机器学习, 卷积神经网络, 长短期记忆