Click-through Rate Prediction Based on Deep Belief Nets and Its Optimization

With the rapid development of Internet advertising, how to predict the target user's click-through rate of Internet advertisement has become a key technology for accurate advertising and has become a hot topic in the field of computational advertising and the application of deep neural networks. To improve the accuracy of CTR (click-through rate) prediction, this work proposed a prediction model based on deep belief nets and studied the influence of the number of hidden layers and the number of units in each layer on prediction results by taking experiments on the 10 million samples in the dataset provided by Kaggle Data Mining platform. In order to solve the problem of training efficiency of deep belief nets in large-scale industrial solutions, this study took wide experiments to prove that there are a lot of stagnation points in the loss function of deep belief nets and it has great negative effect on the training process. To improve the efficiency of training, starting from the characteristics of network loss function, this study further proposed a network optimization fusion model based on stochastic gradient descent algorithm and improved particle swarm optimization algorithm. The fusion algorithm can jump out of the stagnation ground and continue the normal training process. The experiment results show that compared with the traditional prediction model based on gradient boost regression tree and logistic regression, and the deep learning model based on fuzzy deep neural network, the proposed training model has better accuracy in prediction and performs 2.39%, 9.70%, 2.46% and 1.24%, 7.61%, 1.30% better in mean squared error, area under curves, and LogLoss. The fusion method will improve the training efficiency of deep belief nets at the level of 30%~70%. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.