Deep learning has achieved good results in human action recognition, but it still needs to make full use of video human appearance information and motion information. To recognize human actions by using spatial information and temporal information in video, a video human action recognition model based on spatio-temporal two-stream was proposed. Two convolutional neural networks were used to extract spatial and temporal features of video sequences respectively in the proposed model, and then the two neural networks were merged to extract the middle spatio-temporal features, finally the video human action recognition was completed by inputting the extracted features into a 3D convolutional neural network. The video human action recognition experiments were carried out on the data set UCF101 and HMDB51. Experimental results show that the proposed 3D convolutional neural network model based on the spatio-temporal two-stream can effectively recognize the video human actions.

Focusing on the issue that the recommender systems with explicit feedback drastically degrade the accuracy, the recommender technique using probabilistic matrix factorization with explicit and implicit feedback was proposed. So the explicit and implicit feedback was taken into account in this method. Firstly, user trust relationship matrix and user-item matrix were factorized using probabilistic matrix factorization to mix the feedback of user rating records. Then the model was trained to provide users with accurate prediction. The experimental results show that this technique can obtain user preferences effectively and produce large amounts of highly accurate recommendations.