Abstract
In the era of big data information, how to effectively predict and analyze the click-through rate of information advertising is the key for enterprises in various fields to seek returns. The point rate prediction of advertising is one of the core contents of advertising calculation. The traditional shallow prediction model cannot meet the nonlinear relationship of data processing, and the manual processing of data information extraction method is very resource consuming. To solve the above problems, this paper proposes a CNN-LSTM (convolutional neural network-long short-term memory) convolution hybrid neural network algorithm to predict the click-through rate of advertisements. According to the neural network algorithm, the prediction model is constructed, and the effective features are extracted in the process of model establishment, and the prediction analysis is carried out according to the simplified LSTM neural network time serialization features. CNN convolution neural network is used to train the prediction model. This paper analyzes the characteristics of traditional prediction methods and the corresponding solutions and carries out feature learning and prediction model construction for advertising click-through rate prediction. Then, the unknown behavior of advertising users is judged and predicted. The results show that, compared with the single structure network of traditional prediction model, the prediction effect based on CNN-LSTM neural network algorithm has higher accuracy.
Highlights
At present, there are many research methods for advertising click-through rate prediction, such as predicting advertising click-through rate according to neural network algorithm and logical regression algorithm, and constructing quasi Newton training model [6]. is model is easy to analyze and explain the effect of the algorithm and usually acts on the basic standard of advanced model
Based on the above problems, we propose to use the time series function of LSTM neural network algorithm to predict the frequency of events [12]
Experimental Results Analysis of Convolution Hybrid Neural Network Algorithm Based on CNN-LSTM
Summary
There are many research methods for advertising click-through rate prediction, such as predicting advertising click-through rate according to neural network algorithm and logical regression algorithm, and constructing quasi Newton training model [6]. is model is easy to analyze and explain the effect of the algorithm and usually acts on the basic standard of advanced model. 2. Research on Advertising Click-Through Rate Prediction Technology Based on CNN-LSTM Neural Network Algorithm LSTM simplified neural network algorithm has the effect of time reduction and can use less comparative data for prediction and analysis [25].
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