Abstract:The traditional recommendation method based on collaborative filtering can mine the hidden features in the score, but the recommendation process takes a long time, and the score matrix is sparse, resulting in a large error between the real value of the sample and the predicted value. Neural network can quickly calculate the characteristics of objects through batch training. The number of parameters is significantly reduced by the local perception and parameter sharing of convolutional neural network. The calculation time can be shortened by using common neural network and convolutional neural network to realize recommendation together. By adjusting the parameters of neural network, the reasonable feature vector and convolution kernel size for convolutional neural network can be designed to improve recommendation speed and accuracy. Experimental results show that the method of combining neural network with convolutional neural network can reduce the mean value of absolute error of recommendation to below 0.67, and greatly improve the accuracy and effectiveness of recommendation.