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A user-portraits-based recommendation algorithm for traditional short video industry and security management of user privacy in social networks

Author

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  • Miao, Ruomu
  • Li, Benqian

Abstract

With the development of society, social networks have been integrated into people's daily life and work, as well as in mainstream communication tools. However, the current socializing online platforms are imperfect, and users cannot fully protect their privacy. As a result, there are many undesirable privacy leak issues in the social process. This paper aims to promote the short video industry and enhance the security management effect of the short video network platform on user privacy. First, a user-portrait-based recommendation algorithm is comprehensively discussed. Second, the application model of the user portrait under the short video is expounded. Finally, relevant hypotheses are proposed using user portraits for the development and privacy management of platforms, which are verified through experiments. The results show that the four types of platforms have a satisfaction rate of >75 % for user-recommended videos. This research contributes to the development of these types of platforms and users' privacy and security management in the online world.

Suggested Citation

  • Miao, Ruomu & Li, Benqian, 2022. "A user-portraits-based recommendation algorithm for traditional short video industry and security management of user privacy in social networks," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:tefoso:v:185:y:2022:i:c:s0040162522006242
    DOI: 10.1016/j.techfore.2022.122103
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    References listed on IDEAS

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    1. Ricarda Schauerte & Stéphanie Feiereisen & Alan J. Malter, 2021. "What does it take to survive in a digital world? Resource-based theory and strategic change in the TV industry," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(2), pages 263-293, June.
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    4. Xinwei Ren & Wei Yang & Xianliang Jiang & Guang Jin & Yan Yu, 2022. "A Deep Learning Framework for Multimodal Course Recommendation Based on LSTM+Attention," Sustainability, MDPI, vol. 14(5), pages 1-14, March.
    5. Sánchez-Cartas, J. Manuel, 2022. "Welfare and fairness in free-to-play video games," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    6. Yanbo Chen & Jingsha He & Wei Wei & Nafei Zhu & Cong Yu, 2021. "A Multi-Model Approach for User Portrait," Future Internet, MDPI, vol. 13(6), pages 1-14, May.
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    Cited by:

    1. Arenas, Álvaro & Ray, Gautam & Hidalgo, Antonio & Urueña, Alberto, 2024. "How to keep your information secure? Toward a better understanding of users security behavior," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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