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Research on Personalized Push of Mobile Education Resources Based on Mobile Social Network Big Data

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Advanced Hybrid Information Processing (ADHIP 2023)

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Abstract

Nowadays, mobile social networks and mobile educational resources have become two mainstream directions in internet applications. How to combine these two to achieve more personalized and intelligent learning resource push has become a relatively important research direction. Therefore, the research on personalized push of mobile education resources based on mobile social network Big data is proposed. Starting from the mobile social network, the user interest model is constructed by using Big data analysis technology, machine learning, recommendation system and other technical means, and it is matched with the mobile education resource database to achieve personalized recommendation of education resources. The experimental results demonstrate that this method has high recommendation accuracy, with a recommendation accuracy of 95%. It can provide intelligent and efficient learning resource push solutions for mobile learning, promoting the development of mobile learning.

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Aknowledgement

The 2021 Social Science Foundation Project of Nanning University, titled “Research on the Factors Influencing the Leadership of the Director of the Teaching and Research Office of Nanning University,” with fund number 2021JSGC10.

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Authors and Affiliations

  1. College of Digital Economics, Nanning University, Nanning, 530200, China

    Huibing Cao

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  1. Huibing Cao
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Correspondence to Huibing Cao .

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Editors and Affiliations

  1. Harbin Engineering University, Harbin, China

    Lin Yun

  2. Harbin Engineering University, Harbin, China

    Jiang Han

  3. Harbin Engineering University, Harbin, China

    Yu Han

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Cao, H. (2024). Research on Personalized Push of Mobile Education Resources Based on Mobile Social Network Big Data. In: Yun, L., Han, J., Han, Y. (eds) Advanced Hybrid Information Processing. ADHIP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-031-50543-0_31

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  • DOI: https://doi.org/10.1007/978-3-031-50543-0_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50542-3

  • Online ISBN: 978-3-031-50543-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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