基于深度神经网络的多因素感知终端换机预测模型

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

浙江大学学报(工学版)

2021, Vol. 55

Issue (1): 109-115 DOI: 10.3785/j.issn.1008-973X.2021.01.013

计算机技术、自动控制技术

基于深度神经网络的多因素感知终端换机预测模型

陈纬奇1(

),王敬昌2,陈岭1,*(

),杨勇勤3,吴勇2

1. 浙江大学计算机科学与技术学院，浙江杭州 310027
2. 浙江鸿程计算机系统有限公司，浙江杭州 310009
3. 中国电信浙江分公司，浙江杭州 310040

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

Wei-qi CHEN1(

),Jing-chang WANG2,Ling CHEN1,*(

),Yong-qin YANG3,Yong WU2

1. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
2. Zhejiang Hongcheng Computer Systems Limited Company, Hangzhou 310009, China
3. Zhejiang Branch of China Telecom Limited Company, Hangzhou 310040, China

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摘要：

针对基于特征工程的传统终端换机预测模型依赖于领域知识且无法充分利用用户通话、流量使用等序列数据的问题，提出基于深度神经网络的多因素融合终端换机预测模型. 该模型使用长短时记忆网络（LSTM）提取用户通话、流量使用行为序列特征，使用全连接网络融合用户自然属性、行为序列特征和历史换机信息，预测用户是否换机. 实验表明，基于深度神经网络的多因素融合终端换机预测模型能够考虑影响用户换机的多种因素，充分挖掘用户通话、流量使用行为序列特征；当召回率为0.135时，相比于传统模型精确率提高了34.3%.

关键词： 终端换机预测; 多因素感知; 深度神经网络; 长短时记忆网络; 全连接网络

Abstract:

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.

Key words: mobile terminal replacement prediction multi-factor aware deep neural network long short-term memory network fully connected neural network

收稿日期: 2019-12-18 出版日期: 2021-01-05

CLC:

TP 491

基金资助: 国家重点研发计划资助项目（2018YFB0505000）；中央高校基本科研业务费专项资金资助项目（2020QNA5017）

通讯作者: 陈岭 E-mail: vc12301@gmail.com;lingchen@cs.zju.edu.cn

作者简介: 陈纬奇（1995—），男，硕士生，从事数据挖掘的研究. orcid.org/0000-0001-9478-4683. E-mail： vc12301@gmail.com

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引用本文:

陈纬奇,王敬昌,陈岭,杨勇勤,吴勇. 基于深度神经网络的多因素感知终端换机预测模型[J]. 浙江大学学报(工学版), 2021, 55(1): 109-115.

Wei-qi CHEN,Jing-chang WANG,Ling CHEN,Yong-qin YANG,Yong WU. Prediction model of multi-factor aware mobile terminal replacement based on deep neural network. Journal of ZheJiang University (Engineering Science), 2021, 55(1): 109-115.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.01.013 或 http://www.zjujournals.com/eng/CN/Y2021/V55/I1/109

图 1 换机行为与用户特征相关性

图 2 终端换机预测模型框架

表 1 用户自然属性和终端属性说明

图 3 终端品牌使用分布

图 4 LSTM网络结构

表 2 模型超参数设置

图 5 LSTM输出维度d的影响

表 3 忽略不同因素对模型性能的影响

表 4 用户行为数据统计特征

表 5 召回率为0.135时不同模型的精确率和F1

图 6 不同模型的精确率-召回率曲线

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