User churn prediction hierarchical model based on graph attention convolutional neural networks

Mei Miao, Tang Miao, Zhou Long
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Abstract

The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era, the quick development of telecommunications services, the implementation of the number portability policy, and the intensifying competition among operators. At the same time, users' consumption preferences and choices are evolving. Excellent churn prediction models must be created in order to accurately predict the churn tendency, since keeping existing customers is far less expensive than acquiring new ones. But conventional or learning-based algorithms can only go so far into a single subscriber's data; they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features. Additionally, the current churn prediction models have a high computational burden, a fuzzy weight distribution, and significant resource economic costs. The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures, ignoring the reference value supplied by other users with the same package. This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network (GAT-CNN) to address the aforementioned issues. The main contributions of this paper are as follows: Firstly, we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device, edge, and cloud layers. Second, we extend the use of users' own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously. Lastly, we build an integrated offline-online system for churn prediction based on the strengths of the two models, and we experimentally validate the efficacy of cloud-side collaborative training and inference. In summary, the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.
基于图注意卷积神经网络的用户流失预测分层模型
随着移动互联网时代智能手机的日益普及、电信业务的快速发展、号码携带政策的实施以及运营商之间竞争的加剧,电信行业对潜在用户流失的意识日益增强。与此同时,用户的消费偏好和选择也在不断变化。要想准确预测用户流失趋势,就必须建立优秀的用户流失预测模型,因为留住现有用户的成本远远低于获取新用户的成本。但是,传统算法或基于学习的算法只能深入研究单个用户的数据,无法考虑用户订阅的变化,也无法忽略各种特征之间的耦合和相关性。此外,目前的用户流失预测模型计算量大、权重分布模糊、资源经济成本高。目前使用的涉及网络模型的预测算法主要考虑的是用户之间以文字和图片共享的私人信息,而忽略了使用相同套餐的其他用户提供的参考价值。针对上述问题,本文提出了一种基于图注意卷积神经网络(GAT-CNN)的用户流失预测模型。本文的主要贡献如下:首先,我们提出了一个三层分级的云-边缘合作框架,通过设备层、边缘层和云层的两次聚合来增加用户特征输入量。其次,我们通过引入自我关注和图卷积模型来扩展用户自身数据的使用,从而同时跟踪用户和软件包的相对变化。最后,我们基于这两个模型的优势,建立了一个线下线上一体化的用户流失预测系统,并通过实验验证了云端协同训练和推理的有效性。总之,本文提出的基于图注意卷积神经网络的用户流失预测模型能有效解决传统算法的弊端,为电信运营商制定用户留存策略、降低运营成本提供重要的决策支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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