Prediction model of multi-factor aware mobile terminal replacement based on deep neural network

被引：0

作者：

Chen W.-Q. ^{[1
]}

Wang J.-C. ^{[2
]}

Chen L. ^{[1
]}

Yang Y.-Q. ^{[3
]}

Wu Y. ^{[2
]}

机构：

[1] College of Computer Science and Technology, Zhejiang University, Hangzhou

[2] Zhejiang Hongcheng Computer Systems Limited Company, Hangzhou

[3] Zhejiang Branch of China Telecom Limited Company, Hangzhou

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2021年 / 55卷 / 01期

关键词：

Deep neural network; Fully connected neural network; Long short-term memory network; Mobile terminal replacement prediction; Multi-factor aware;

D O I：

10.3785/j.issn.1008-973X.2021.01.013

中图分类号：

学科分类号：

摘要：

A multi-factor aware mobile terminal replacement prediction model based on deep neural networks was proposed to address the problem that traditional mobile terminal replacement prediction models based on feature engineering rely on the domain knowledge and cannot sufficiently use user's call details and data traffic details. Long short-term memory (LSTM) networks were utilized to extract the sequence characteristics of user's call and data traffic behaviors. Then a fully connected neural network was utilized to fuse user's natural attributes, sequence characteristics, and historical terminal replacement information for prediction. The experimental results show that the proposed model can consider multiple factors affecting terminal replacement and sufficiently exploit the sequence characteristics of user's call details and data traffic details. The precision was increased by 34.3% compared with traditional methods when recall was set to 0.135. Copyright ©2021 Journal of Zhejiang University (Engineering Science). All rights reserved.

引用

页码：109 / 115

页数：6

共 19 条

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