Xu Jiabing, Liu Jiarui, Yao Tianen, Li Yang
Yantai Institute of China Agricultural University, Yantai, China.
School of Business, The University of Sydney, Sydney, Australia.
Big Data. 2023 Oct;11(5):355-368. doi: 10.1089/big.2021.0365. Epub 2023 Jan 19.
This study aims to transform the existing telecom operators from traditional Internet operators to digital-driven services, and improve the overall competitiveness of telecom enterprises. Data mining is applied to telecom user classification to process the existing telecom user data through data integration, cleaning, standardization, and transformation. Although the existing algorithms ensure the accuracy of the algorithm on the telecom user analysis platform under big data, they do not solve the limitations of single machine computing and cannot effectively improve the training efficiency of the model. To solve this problem, this article establishes a telecom customer churn prediction model with the help of backpropagation neural network (BPNN) algorithm, and deploys the MapReduce programming framework on Hadoop platform. Using the data of a telecom company, this article analyzes the loss of telecom customers in the big data environment. The research shows that the accuracy of telecom customer churn prediction model in BPNN is 82.12%. After deploying large data sets, the learning and training time of the model is greatly shortened. When the number of nodes is 8, the acceleration ratio of the model remains at 60 seconds. Under big data, the telecom user analysis platform not only ensures the accuracy of the algorithm, but also solves the limitations of single machine computing and effectively improves the training efficiency of the model. Compared with that of the existing research, the accuracy of the model is improved by 25.36%, and the running time is shortened by about twice. This business model based on BPNN algorithm has obvious advantages in processing more data sets, and has great reference value for the digital-driven business model transformation of the telecommunications industry.
本研究旨在将现有的电信运营商从传统互联网运营商转变为数字驱动型服务企业，提升电信企业的整体竞争力。数据挖掘应用于电信用户分类，通过数据集成、清理、标准化和转换来处理现有的电信用户数据。尽管现有算法在大数据下的电信用户分析平台上保证了算法的准确性，但它们没有解决单机计算的局限性，无法有效提高模型的训练效率。为解决这一问题，本文借助反向传播神经网络（BPNN）算法建立了电信客户流失预测模型，并在Hadoop平台上部署了MapReduce编程框架。利用某电信公司的数据，本文分析了大数据环境下电信客户的流失情况。研究表明，BPNN中电信客户流失预测模型的准确率为82.12%。部署大数据集后，模型的学习和训练时间大大缩短。当节点数为8时，模型的加速比保持在60秒。在大数据环境下，电信用户分析平台不仅保证了算法的准确性，还解决了单机计算的局限性，有效提高了模型的训练效率。与现有研究相比，模型的准确率提高了25.36%，运行时间缩短了约一半。这种基于BPNN算法的商业模式在处理更多数据集方面具有明显优势，对电信行业的数字驱动型商业模式转型具有重要参考价值。
