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Research of Personalized Recommendation System Based on Multi-view Deep Neural Networks

  • Yunfei ZiEmail author
  • Yeli LiEmail author
  • Huayan SunEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

In recent years, deep learning has made leaps in the fields of artificial intelligence, machine learning and so on, especially in the fields of speech recognition, image recognition and self-learning. 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, and 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 depth neural network, self-learning and other advantages to achieve the model of personalized information, the model through the input multi-source heterogeneous data characteristics of in-depth neural network learning, extraction fusion collaborative filtering widely personalized generation candidate sets, and then through two of models to learn to produce a sort set, Then realize accurate, real-time, personalized recommendation. The experimental results show that the model can study and extract the user’s implicit feature well, and 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.

Keywords

Deep neural networks Personalized recommendation Collaborative filtering Candidate set Sort set Multi-view 

Notes

Acknowledgment

Fund project: National Natural Science Foundation of China (11603004), Beijing Natural Science Foundation (1173010), Beijing science and technology innovation service ability coordinated innovation project (PXM2016_014223_000025).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Information EngineeringBeijing Institute of Graphic CommunicationBeijingChina

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