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In order to solve the problem of strong popularity bias in recommendation lists generated by Bayesian personalized ranking algorithm under the condition of unbalanced data, this paper designed a hybrid recommendation algorithm based on feature embedding to remove popularity bias. Firstly, this paper used convolutional neural network to extract user and item features, determine user preferences, and fill original unbalanced data according to user preferences. Secondly, this paper embedded the user preference features extracted from convolutional neural network into the Bayesian personalized ranking algorithm for hybrid recommendation. Finally, it trained the mixed recommendation model with score filled data, and obtained the personalized ranking list without popularity bias. In order to verify the performance of the algorithm, it conducted the analysis and comparison experiments on Movielens-100 K and Movielens-1 M. And the experimental results show that the popularity bias is reduced by about 50%～60% and the accuracy is improved by about twice. [ABSTRACT FROM AUTHOR]

To solve the problem of insufficient real-time performance of existing optical flow estimation methods, this paper proposed a lightweight improved PSC-NET model with deep separable convolution ( depth pyramid, warping and cost volume, DS-PWC). One of the improvements was decoupling the conventional two-dimensional convolutional network layer into a deep separable convolutional layer. The other one was adding a weight coefficient based on the number of layers in the pyramid layer by DS-PWC, which greatly reduced the number of model parameters in the network structure without loss of precision. In addition, in the training process, the paper applied data enhancement technologies such as I+ ORE to further improve the generalization ability of estimation and prediction results. The experimental results show that the DS-PWC model was tested in the dataset and the operating efficiency reaches about 58 fps while maintaining the quality. To verify the effectiveness of the algorithm, this paper carried out the ablation experiments of model structure and data enhancement. The results verify the effectiveness of the DS-PWC model. [ABSTRACT FROM AUTHOR]