基于电信大数据的5G网络海量用户复访行为预测模型

doi:10.11959/j.issn.1000-0801.2023026

电信科学 ›› 2023, Vol. 39 ›› Issue (2): 157-162.doi: 10.11959/j.issn.1000-0801.2023026

基于电信大数据的5G网络海量用户复访行为预测模型

孙玉娣

江苏经贸职业技术学院数字商务学院，江苏南京 211168

修回日期:2023-02-07 出版日期:2023-02-20 发布日期:2023-02-01
作者简介:孙玉娣（1981- ），女，江苏经贸职业技术学院数字商务学院副教授，主要研究方向为本体、知识工程
基金资助:
2021年江苏高校“青蓝工程”优秀教学团队项目；江苏经贸职业技术学院“领军人才”资助项目

A prediction model of massive 5G network users’ revisit behavior based on telecom big data

Yudi SUN

School of Digital Commerce, Jiangsu Vocational Institute of Commerce, Nanjing 211168, China

Revised:2023-02-07 Online:2023-02-20 Published:2023-02-01
Supported by:
“Qing Lan Project” in Jiangsu Universities in 2021, “Leading Talents” Program of Jiangsu Vocational Institute of Commerce

摘要/Abstract

摘要：

5G网络中的用户会产生大量的访问数据，导致用户复访行为难以精准预测，因此提出基于电信大数据的5G网络海量用户复访行为预测模型。从电信大数据中提取用户上网历史行为特征数据，构建数据集。引入多阶加权马尔可夫链模型，通过计算各阶自相关系数，得到模型权重值，计算模型的统计量。经过分析后得到各阶步长的马尔可夫氏链一步转移概率矩阵，从而实现对5G网络海量用户复访行为的精准预测。实验结果表明，该模型拥有最低的均值误差和标准差，以及最高的精度、查全率、查准率、F1指标，可证明该方法在预测用户复访行为方面有着非常明显的优势。

关键词: 电信大数据, 用户复访行为预测, 多阶加权马尔可夫链模型, 一步转移概率矩阵, 自相关系数

Abstract:

Users in 5G networks will generate a large amount of access data, which makes it difficult to accurately predict users’ revisit behavior.Therefore, a prediction model of massive 5G network users’ revisit behavior based on telecom big data was proposed.The user’s historical online behavior characteristic data was extracted from the telecom big data to build a data set.Multi order weighted Markov chain model was introduced.The model weight value was obtained by calculating the autocorrelation coefficient of each order, and the statistics of the model were calculated.After analysis, the one-step transition probability matrix of Markov chain with each step size was obtained, so as to accurately predict the revisit behavior of massive users in 5G network.The experimental results show that the proposed model has the lowest mean error and standard deviation, as well as the highest accuracy, recall, precision and F1 indicators, which can prove that the proposed method has a very obvious advantage in predicting users’ revisit behavior.

Key words: telecom big data, prediction of users’ revisit behavior, multi order weighted Markov chain model, one step transition probability matrix, autocorrelation coefficient

中图分类号:

TP357

孙玉娣. 基于电信大数据的5G网络海量用户复访行为预测模型[J]. 电信科学, 2023, 39(2): 157-162.

Yudi SUN. A prediction model of massive 5G network users’ revisit behavior based on telecom big data[J]. Telecommunications Science, 2023, 39(2): 157-162.

图/表 6

表1

图1

表2

图2

图3

表3

参考文献 22

[1]	卢宇红, 宋佳丽, 王萌 ,等. 基于深度神经网络融合稀疏分组 lasso 的预测模型研究[J]. 中国卫生统计, 2021,38(6): 821-827.
	LU Y H , SONG J L , WANG M ,et al. The study on the prediction model based on deep neural network together with sparse group lasso[J]. Chinese Journal of Health Statistics, 2021,38(6): 821-827.
[2]	顾亦然, 王雨, 杨海根 . 基于用户行为序列的短视频用户多行为点击预测模型[J]. 电子与信息学报, 202310.11999/JEIT211458.
	GU Y R , WANG Y , YANG H G . Multi-action click prediction model for short video users based on user’s behavior sequence[J]. Journal of Electronics ＆ Information Technology, 202310.11999/JEIT211458.
[3]	CAO W C , WANG K , GAN H C ,et al. User online purchase behavior prediction based on fusion model of CatBoost and Logit[J]. Journal of Physics:Conference Series, 2021,2003(1): 012011.
[4]	LI H R , LIN F Q , LU X ,et al. Systematic analysis of fine-grained mobility prediction with on-device contextual data[J]. IEEE Transactions on Mobile Computing, 2022,21(3): 1096-1109.
[5]	QIAO S B , PANG S C , WANG M ,et al. Online video popularity regression prediction model with multichannel dynamic scheduling based on user behavior[J]. Chinese Journal of Electronics, 2021,30(5): 876-884.
[6]	NIU B , SUI L , TANG J R ,et al. Prediction of microblog users’ forwarding behavior based on interactive and active information[C]// Proceedings of the 2020 International Conference on Aviation Safety and Information Technology. New York:ACM Press, 2020: 554-559.
[7]	XIAO Y P , LI J H , ZHU Y F ,et al. User behavior prediction of social hotspots based on multimessage interaction and neural network[J]. IEEE Transactions on Computational Social Systems, 2020,7(2): 536-545.
[8]	HU G Y , ZHOU Z J , HU C H ,et al. Hidden behavior prediction of complex system based on time-delay belief rule base forecasting model[J]. Knowledge-Based Systems, 2020,203: 106147.
[9]	SUDAN B , CANSIZ S , OGRETICI E ,et al. Prediction of success and complex event processing in E-learning[C]// Proceedings of 2020 International Conference on Electrical,Communication,and Computer Engineering (ICECCE). Piscataway:IEEE Press, 2020: 1-6.
[10]	SOLTANI N Y , . Online learning of sparse Gaussian conditional random fields with application to prediction of energy consumers behavior[C]// Proceedings of 2021 IEEE Statistical Signal Processing Workshop (SSP). Piscataway:IEEE Press, 2021: 486-490.
[11]	SUN L T , GAO S W , WANG L . An automatic test sequence generation method based on Markov chain model[C]// Proceedings of 2021 World Conference on Computing and Communication Technologies (WCCCT). Piscataway:IEEE Press, 2021: 91-96.
[12]	DENNIS L A , FU Y , SLAVKOVIK M . Markov chain model representation of information diffusion in social networks[J]. Journal of Logic and Computation, 2022,32(6): 1195-1211.
[13]	PENG L , WEN L , QIANG L ,et al. Research on complexity model of important product traceability efficiency based on Markov chain[J]. Procedia Computer Science, 2020,166: 456-462.
[14]	HAN C , CHEN J , TAN M K ,et al. A tensor-based Markov chain model for heterogeneous information network collective classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2022,34(9): 4063-4076.
[15]	CRUZ I R , LINDSTR?M J ,, TROFFAES M C M ,et al. Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis[J]. Computational Statistics ＆ Data Analysis, 2022,176: 107558.
[16]	ALAMOUDI A , LIU M L , PAYANI A ,et al. Predicting mobile users traffic and access-time behavior using recurrent neural networks[C]// Proceedings of 2021 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway:IEEE Press, 2021: 1-6.
[17]	LIU K , TATINATI S , KHONG A W H . A weighted feature extraction technique based on temporal accumulation of learner behavior features for early prediction of dropouts[C]// Proceedings of 2020 IEEE International Conference on Teaching,Assessment,and Learning for Engineering (TALE). Piscataway:IEEE Press, 2021: 295-302.
[18]	SETIA S , JYOTI V , DUHAN N . HPM:a hybrid model for user’s behavior prediction based on N-gram parsing and access logs[J]. Scientific Programming, 2020: 1-18.
[19]	CHEN L Y , WANG L H , ZHOU Y X . Research on data mining combination model analysis and performance prediction based on students’ behavior characteristics[J]. Mathematical Problems in Engineering, 2022: 1-10.
[20]	RASOULI A , ROHANI M , LUO J . Bifold and semantic reasoning for pedestrian behavior prediction[C]// Proceedings of 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2022: 15580-15590.
[21]	ZHOU H , YU K M , CHEN Y C ,et al. A hybrid feature selection method RFSTL for manufacturing quality prediction based on a high dimensional imbalanced dataset[J]. IEEE Access, 2021,9: 29719-29735.
[22]	JIANG L , LIU H , JIANG H ,et al. Heuristic and neural network based prediction of project-specific API member access[J]. IEEE Transactions on Software Engineering, 2022,48(4): 1249-1267.

数据类型	具体内容	数据来源
用户浏览数据	用户访问的页面总数、页面的用户访问量	用户行为分析日志
用户属性数据	用户账号、密码、单位、IP地址、操作系统、终端	5G网络服务器日志
用户访问行为数据	用户访问页面的频率、时间间隔、访问次数、停留时间以及页面被访问的平均次数	5G网络服务器日志
用户访问深度数据	页面跳出率、用户访问页面的设备和方式	5G网络服务器日志

模型阶数	r_k	w_k
1	0.739 8	0.399 2
2	0.532 6	0.287 4
3	0.373 4	0.201 5
4	0.207 3	0.188 7

指标	本文模型	文献[1]模型	文献[2]模型
P	0.714 1±0.010 5	0.683 3±0.034 4	0.670 5±0.025 9
R	0.550 9±0.007 8	0.524 3±0.008 5	0.510 0±0.007 2
F1	0.621 9±0.001 0	0.594 3±0.018 0	0.547 1±0.017 3
ACC	0.701 8±0.021 0	0.683 8±0.006 1	0.663 1±0.001 3
AUC	0.686 9±0.001 3	0.655 9±0.008 3	0.642 9±0.004 5

基于电信大数据的5G网络海量用户复访行为预测模型

A prediction model of massive 5G network users’ revisit behavior based on telecom big data

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 22

相关文章 1

Metrics

推荐阅读 0