Research on Multidimensional User Experience Evaluation Model Based on Principal Component Analysis

摘要

In the network environment, the multidimensional user experience evaluation model is built to meet the personalized needs of multi-dimensional users and improve the network service instructions for multi-dimensional users. A design method of multidimensional user evaluation model is proposed based on principal component analysis and personalized data mining. The semantic ontology feature directivity clustering method is used to mine the personalized demand data of multi-dimensional users, and the association user adaptive tracking method is used to predict the multi- dimensional user experience, and the principal component analysis is carried out according to the prediction structure. The state recognition and data feature analysis of multidimensional user experience data are realized, the feature decomposition model of multi-dimensional user experience data under mobile computing environment is constructed, and the multidimensional user experience evaluation is realized. The simulation results show that, this method has good accuracy and high satisfaction in multi-dimensional user experience evaluation, which has a good application value in improving the network service.