RETRACTED: Serialized Recommendation Technology Based on Deep Neural Network (Retracted Article)

Since the construction of brain network is like organic brain organization, profound brain network has high effectiveness and high accuracy in separating data from profound elements, fit for multifacet learning, conceptual component portrayal, cross-space learning capacity, multisource, heterogeneous data content. This paper presents brain network models, multifacet perceptrons, RBM models, and convolutional brain organizations. In this paper, the experimental design is carried out according to the principle of the model. Compared with the traditional commonly used CMF, PMF, SVD, and item-based algorithms, the proposed two models have lower root mean square error and higher recall rate. The trial brought about by this paper shows that through investigation, it is trusted that because of the impact of the component of the info information, too many secret layers might prompt overfitting. Accordingly, this paper sets the quantity of stowed away layers in the DNN organization to 2.