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An analytics model for TelecoVAS customers’ basket clustering using ensemble learning approach
Journal of Big Data ( IF 8.1 ) Pub Date : 2021-02-17 , DOI: 10.1186/s40537-021-00421-1
Mohammadsadegh Vahidi Farashah , Akbar Etebarian , Reza Azmi , Reza Ebrahimzadeh Dastjerdi

Value-Added Services at a Mobile Telecommunication company provide customers with a variety of services. Value-added services generate significant revenue annually for telecommunication companies. Providing solutions that can provide customers of a telecommunication company with relevant and engaging services has become a major challenge in this field. Numerous methods have been proposed so far to analyze customer basket and provide related services. Although these methods have many applications, they still face difficulties in improving the accuracy of bids. This paper combines the X-Means algorithm, the ensemble learning system, and the N-List structure to analyze the customer portfolio of a mobile telecommunication company and provide value-added services. The X-Means algorithm is used to determine the optimal number of clusters and clustering of customers in a mobile telecommunication company. The ensemble learning algorithm is also used to assign categories to new Elder customers, and finally to the N-List structure for customer basket analysis. By simulating the proposed method and comparing it with other methods including KNN, SVM, and deep neural networks, the accuracy improved to about 7%.



中文翻译:

使用集成学习方法的TelecoVAS客户购物篮聚类分析模型

移动电信公司的增值服务为客户提供各种服务。增值服务每年为电信公司带来可观的收入。提供能够为电信公司的客户提供相关且引人入胜的服务的解决方案已成为该领域的主要挑战。迄今为止,已经提出了许多方法来分析客户群并提供相关服务。尽管这些方法有许多应用,但它们在提高出价准确性方面仍然面临困难。本文结合了X均值算法,集成学习系统和N-List结构来分析移动电信公司的客户组合并提供增值服务。X-Means算法用于确定移动电信公司中群集的最佳数量和客户群集。集成学习算法还用于将类别分配给新的老客户,最后分配给N-List结构以进行客户篮子分析。通过模拟所提出的方法并将其与其他方法(包括KNN,SVM和深度神经网络)进行比较,该方法的准确性提高到了7%左右。

更新日期:2021-02-17
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