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.

Key words: semi-supervised learning, improved self-training model, poor quality user identification, unlabeled data