Application of improved self-training model in the identification of users with poor service quality

Li YU; Zhe LI; Fei GAO; Xiangyang YUAN; Yong YANG

doi:10.11959/j.issn.1000-0801.2021191

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Application of improved self-training model in the identification of users with poor service quality

Research and Development | 更新时间：2024-06-05

- Application of improved self-training model in the identification of users with poor service quality
- Telecommunications Science Vol. 37, Issue 10, Pages: 136-142(2021)
- 作者机构：
  
  1. 中国移动通信有限公司研究院，北京 100053
  2. 中国移动通信集团公司，北京 100033
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021191
  CLC： TN915.41
- Published Online：2021-10，
  
  Published：20 October 2021
- 稿件说明：
移动端阅览
Li YU, Zhe LI, Fei GAO, et al. Application of improved self-training model in the identification of users with poor service quality[J]. Telecommunications science, 2021, 37(10): 136-142.
DOI：

Li YU, Zhe LI, Fei GAO, et al. Application of improved self-training model in the identification of users with poor service quality[J]. Telecommunications science, 2021, 37(10): 136-142. DOI： 10.11959/j.issn.1000-0801.2021191.

摘要

质差用户识别是降低用户投诉率、提升用户满意度的重要环节。针对当前电信网络系统中业务感知相关的大量结构化及非结构化数据难以有效标注、质差用户标签不完备、现有监督学习模型训练样本不均衡而导致质差识别率低的问题，采用改进自训练半监督学习模型，利用少量满意度低分和投诉用户作为质差用户标签对网络数据进行标注，并通过标签迁移对大量未标注数据进行训练识别质差用户。实验表明，相比于识别准确率高但是训练成本高的全监督学习和识别准确率低的无监督学习，半监督学习可以充分利用无标签样本数据进行有效训练，保证较低训练成本的同时显著提升质差用户识别准确率。

Abstract

Poor quality user identification is an important method to reduce the complaint rate and increase satisfaction.It is difficult to effectively label a large amount of structured and unstructured data related to business perception in current telecommunications network systems

poor quality user labels are not complete

and the existing supervised learning model training samples are unbalanced

resulting in a low quality recognition rate.An improved self-training semi-supervised learning model was adopted

a small number of low-satisfaction and complaint users as poor quality user labels was used to label network data

and label migration was used to train a large amount of unlabeled data to identify poor quality users.Experiments show that compared to fully supervised learning with high recognition model accuracy but high training cost and unsupervised learning with low recognition model accuracy

semi-supervised learning can make full use of unlabeled sample data for effective training

ensuring lower training costs and the recognition accuracy of poor-quality users is significantly improved.

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