Abstract:In view of the most existing user attribute prediction methods based on application that is less considered actual use time of application in the foreground

put forward average use time of application in the foreground

At the same time

the Co-training framework based on sparse autoencoder and neural network is adopted

make full use of a large number of unlabeled data

predict user attribute from application category and average time used of application in the foreground. When the experiment is carried out

first the network is initialized with unlabeled data

then the gradient descent algorithm based on accuracy is used to update the parameters. Experimental results show that the proposed algorithm improves the accuracy of user attributes prediction.