Application of high performance computing and deep neural network learning in intelligent city mechanical product recommendation

Intelligent city is a product of the deep integration of information technology, industrialization and urbanization, which has a large number of intelligent mechanical products. The users widely evaluate their application characteristics, and the selection of mechanical products based on user evaluation has become a trend. Nowadays, personalized mechanical product recommendation based on user evaluation is more and more widely used. However, due to the sparse evaluation data, the recommendation accuracy needs to be improved. In this paper, the principle of matrix decomposition is deeply analyzed in order to provide useful ideas for solving this problem. The bias weight hybrid recommendation model of user preference and rating object characteristics is proposed, and the corresponding hybrid recommendation algorithm is designed. First, estimated data obtained using the matrix decomposition principle is supplemented to the sparse data matrix. Secondly, according to the characteristics of users and ratings, initial positions were set based on the statistical distribution of high-performance computing data, and bias weights were set by incorporating each feature. Finally, the nonlinear learning ability of deep neural network learning is used to enhance the classification effectiveness. Practice has proved that the constructed model is reasonable, the designed algorithm converges fast, the recommendation accuracy is improved by about 10%, and the model better alleviates the problem of sparse scoring data. The practical application is simple and convenient, and has good application value.