论文标题
移动网络中投诉热点问题的明智预测
Smart Prediction of the Complaint Hotspot Problem in Mobile Network
论文作者
论文摘要
在移动网络中，投诉热点问题通常会影响数千个用户的服务，并导致巨大的经济损失和批量投诉。在本文中，我们提出了一种基于实时用户信号数据的客户投诉的方法。通过分析网络和用户SEVICE过程，在从S1接口收集的XDR数据中提取了30个与用户体验相关的关键数据字段。此外，我们通过衍生功能来增强这些基本功能，以供用户体验评估，例如单次击打功能，统计功能和差异功能。考虑到不平衡数据的问题，我们将LightGBM用作我们的预测模型。 LightGBM具有强大的概括能力，旨在处理不平衡的数据。我们进行的实验证明了该提案的有效性和效率。该方法已被部署用于日常工作，以找到热门投诉问题范围以及报告受影响的用户和区域。
In mobile network, a complaint hotspot problem often affects even thousands of users' service and leads to significant economic losses and bulk complaints. In this paper, we propose an approach to predict a customer complaint based on real-time user signalling data. Through analyzing the network and user sevice procedure, 30 key data fields related to user experience have been extracted in XDR data collected from the S1 interface. Furthermore, we augment these basic features with derived features for user experience evaluation, such as one-hot features, statistical features and differential features. Considering the problems of unbalanced data, we use LightGBM as our prediction model. LightGBM has strong generalization ability and was designed to handle unbalanced data. Experiments we conducted prove the effectiveness and efficiency of this proposal. This approach has been deployed for daily routine to locate the hot complaint problem scope as well as to report affected users and area.
