Research and Application of User Satisfaction Model Based on Signaling Mining

With the rapid development of mobile communication and Internet technology, operators' data services are bringing scientific breakthroughs that can produce chain reaction, resulting in a number of innovative Internet products. With the increasing demands of operator's products and services, the amount of user-related complaints is also growing rapidly. This not only puts negative impacts on operator business innovation, but also leads to a continuous decrease in user satisfaction. In order to address this issue, we propose a user satisfaction assessment based on signaling mining. This assessment analyzes the signaling data of the users who complain at different time and performs regionalization. Finally, a user complaint prediction model based on GBDT(Gradient Boosting Decision Tree) algorithm is established to output the user complaint probability. The experimental results show that our proposed model is 15% higher than the state-of-art complaint models on the AUC indicator. This provides a positive data reference for predicting user complaints, identifying high-risk users, and improving user satisfaction.