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

作　　者：孙玉娣[1] SUN Yudi(School of Digital Commerce,Jiangsu Vocational Institute of Commerce,Nanjing 211168,China)

机构地区：[1]江苏经贸职业技术学院数字商务学院,江苏南京211168

出　　处：《电信科学》2023年第2期157-162,共6页Telecommunications Science

基　　金：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。

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。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 tele-com 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 calcu-lated.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.

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 

分 类 号：TP357[自动化与计算机技术—计算机系统结构]