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Top-N recommendation algorithm integrated neural network

  • S.I. : SPIoT 2020
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the gradual popularization of social informatization, people’s information security is gradually threatened. The development of the Internet has gradually exposed people’s privacy, and the protection of the privacy of people in the era of Internet information has become a topic of concern to all people. This research mainly discusses the research of the Top-N recommendation algorithm with integrated neural network. The purpose of protecting people’s privacy is achieved by interfering with the Top-N recommendation algorithm on the Internet signal. In response to people’s concerns, the Top-N recommendation algorithm with integrated neural network was used during the experiment. The experimenters were randomly selected from netizens who frequently used computers to measure the privacy and security of each group of researchers and the signal of the Top-N recommendation algorithm. Interference level. The use of the Top-N recommendation algorithm is divided into six levels, and the experimentally measured information protection rate is 88% when the use level of the Top-N recommendation algorithm is F level. In the case of signal interference, the interference intensity is divided into five levels. Similarly, when the signal interference intensity is 5, the information leakage rate is at least 10%. The selection of personnel throughout the experiment is random and the interference during the experiment and the use of the Top-N recommendation algorithm with integrated neural network are divided according to levels. The research results show that when the signal interference intensity is 5 and the recommended algorithm is F, the privacy protection of netizens is the best. The Top-N recommendation algorithm with integrated neural network has important potential value in protecting people’s privacy.

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Acknowledgements

This work was supported by Doctoral Research Projects of Guizhou Normal University (program number: GZNUD [2017]36).

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Authors and Affiliations

  1. School of Economics and Management, Guizhou Normal University, Guiyang, 550001, China

    Liang Zhang

  2. School of Aerospace Engineering, Tsinghua University, Bei**g, 100084, China

    Liang Zhang

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  1. Liang Zhang
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Correspondence to Liang Zhang.

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Zhang, L., Zhang, L. Top-N recommendation algorithm integrated neural network. Neural Comput & Applic 33, 3881–3889 (2021). https://doi.org/10.1007/s00521-020-05452-y

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  • DOI: https://doi.org/10.1007/s00521-020-05452-y

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