Research of personalized recommendation system based on deep neural network

The deep neural network is similar to the biological neural network, so it has the ability of high efficiency and accurate extraction of the deep hidden features of information, can learn multiple layers of abstract features, and can learn more about cross-domain, multi-source and heterogeneous content information. This paper presents an extraction feature based on multi-user-project combined deep neural network, self-learning and other advantages to achieve the model of personalized information. This model does deep neural network self-learning and extraction based on the input multi-source heterogeneous data characteristics，fuses collaborative filtering wide personalization to generate candidate sets, and then through two times of model self-learning produces a sort set. Finally，it can achieve accurate, real-time, and personalized recommendations. The experimental results show that the model can self-learn and extract the user′s implicit feature well, and it can solve the problems of sparse and new items of traditional recommendation system to some extent, and realize more accurate, real-time and personalized recommendation.