QoE prediction on imbalanced IPTV data based on multi-layer neural network
Chaoping Lv, Ruochen Huang, Wenqin Zhuang, Xin Wei, Qiuxia Bao · 2017
IPTV is a new multimedia service over the Internet. The rapid development of IPTV makes the assessment of quality of experience in IPTV a hot topic to the service providers. In this paper, we study the relationship between the record of some viewing parameters from the IPTV set-top box and the users' Quality of Experience (QoE). Firstly, we analyze the data and choose some important attributions and then map the trouble tickets table to QoE representing acceptable or unacceptable. According to the imbalanced feature of the dataset, we proposed the multi-layer neural network using BP algorithm based on SGD for prediction of QoE. To avoid overfitting, we apply dropout method to the model when training the dataset. At last, we compare the proposed model to SVM and Decision Tree. Experimental results show that the proposed methods can indeed improve the accuracy of QoE prediction.