Abstract:With the development of Internet technology,the problem of information overload needs to be solved.Personalized recommendation technology has become a powerful tool to solve the problem of information overload.In order to provide more accurate commodity recommendation service for users,a deep neural network model (SDAE-BPR) based on the combination of stack denoising auto-encoder(SDAE) and Bayesian personalized ranking(BPR) is proposed.Firstly,SDAE is used to take the commodity score data as input,and the hidden features are obtained after coding.Secondly,the implicit feature vectors of the corresponding commodities are learned by the method of BPR.This model can avoid the influence of matrix sparsity,thus achieving the effect of more accurate commodity recommendation.Finally,based on Movielens 20 M data set,the results of SDAE-BPR are compared with the traditional item-based collaborative filtering model(IB-CF) and the traditional user-based collaborative filtering model(UB-CF).It can be seen that SDAE-BPR has higher accuracy.