关键词: 推荐系统, 信息爆增, 矩阵分解, 卷积神经网络

Abstract: Model research and improvement are carried out on issues such as sparse recommendation data scoring and evaluation information explosion. Firstly, based on the traditional matrix decomposition model, the paper adds the influence factors of users and projects to improve the generalization ability of the prediction model. Secondly, this paper establishes a cross-channel convolutional neural network to identify user evaluation information, combines the improved matrix decomposition model with the improved convolutional neural network, and proposes a combination of improved matrix decomposition and cross-channel convolutional neural network, model to improve the accuracy of the predictive model. The experimental results show that the optimal performance of the model is 2.96%, 10.27% and 1.77% higher than that of PMF, CTR and CDL in three data sets respectively. The performance of MF&CNN is increased by 0.29%, 2.98% and 0.08% respectively. When the data density increases from 20% to 80%, the model predicts performance will be further improved.

Key words: recommended system, Information explosion, matrix factorization, convolution neural network