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The Mobile APP Conversion Rate Prediction Based on Improved Wide&Deep of Interactive Feature Extraction
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[1]SUN Xiaoyan,NIE Xin,BAO Lin,et al.The Mobile APP Conversion Rate Prediction Based on Improved Wide&Deep of Interactive Feature Extraction[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):26-32.[doi:10.13705/j.issn.1671-6833.2020.06.005]
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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 41卷 Number of periods: 2020 06 Page number: 26-32 Column: Public date: 2020-12-31
Title:
The Mobile APP Conversion Rate Prediction Based on Improved Wide&Deep of Interactive Feature Extraction
Author(s):
SUN Xiaoyan;  NIE Xin;  BAO Lin;  CHEN Yang
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China
Keywords:
conversion rate prediction;  feature interaction;  field-aware factorized machine;  Wide&; Deep;  mobile APP
CLC:
-
DOI:
10.13705/j.issn.1671-6833.2020.06.005
Abstract:
The conversion rate prediction (CRP) of mobile APP advertising was challenging due to the high-dimension, sparsity, and high interactions. Motivated by this an improved Wide&Deep model was proposed by fusing field-aware factorized machine (FFM) and deep convolutional neural network (DCNN) to effectively and automatically obtain the lower-order and higher-order interactions of high-dimensional sparse features, so as to realize the automatic and efficient combination of features and improve the accuracy of CRP. The framework of the proposed algorithm was first delivered, and a feature combination algorithm based on the width module FFM to extract the interactive relations of lower-order features was presented for the embedded sparse data. The extraction of the higher-order interactive features based on a DCNN was further given by fusing the latent features obtained by the FFM. Finally, the interactive feature combinations obtained by width and depth modules were integrated for the CRP. The application of the proposed algorithm in predicting the conversion rate of Tencent′s mobile APP advertisements demonstrated the effectiveness of the method.
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