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Advertising click-through rate prediction modelbased on enhanced FNN(PDF)


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Advertising click-through rate prediction modelbased on enhanced FNN
Yang YantingHan Bin
School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212003,China
click-through rate prediction feature combinations neural network feature generation
In order to further improve the ability of the click-through rate(CTR)prediction model to learn effective feature combinations,this paper proposes an advertising click-through rate prediction model based on enhanced factorization machine supported neural network(EFNN). This model adds a new feature generation layer to factorization machine supported neural network(FNN),and uses a convolution operation for CTR data. After channel transformation of the data,the inception structure is introduced for convolution. The generated new features and original features are combined to improve the learning ability of the deep network. The experimental results prove that the enhanced FNN with new feature generation layer can effectively improve the accuracy of advertising click-through rate prediction.


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Last Update: 2020-02-29