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Design of Personalized Employment Guidance System for College Students Based on Big Data

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e-Learning, e-Education, and Online Training (eLEOT 2021)

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Abstract

In view of the traditional system using personality test for employment guidance, which leads to one-sided guidance results, outdated system design architecture and low operation efficiency, this paper designs a personalized employment guidance system for college students based on big data. After designing the system hardware to collect the information of students’ employment environment, the system framework is designed based on B/S structure. Tptmf algorithm is used to recommend employment resources for users. After analyzing the system requirements, the database is designed to realize the system functions. The simulation results show that the response time of employment guidance system using big data is short, and the highest server occupation is only 11%, which is feasible.

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Author information

Authors and Affiliations

  1. Admission and Employment Office, Dongying Vocational Institute, Dongying, 257090, China

    Mei-bin Qi

  2. **nyu University, **nyu, 338004, Jiangxi, China

    Yu-juan Zhang

Authors
  1. Mei-bin Qi
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  2. Yu-juan Zhang
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Corresponding author

Correspondence to Yu-juan Zhang .

Editor information

Editors and Affiliations

  1. Hunan Normal University, Changsha, China

    Weina Fu

  2. Hunan Normal University, Changsha, China

    Shuai Liu

  3. Hunan Normal University, Changsha, China

    Jianhua Dai

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Qi, Mb., Zhang, Yj. (2021). Design of Personalized Employment Guidance System for College Students Based on Big Data. In: Fu, W., Liu, S., Dai, J. (eds) e-Learning, e-Education, and Online Training. eLEOT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-030-84383-0_26

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  • DOI: https://doi.org/10.1007/978-3-030-84383-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84382-3

  • Online ISBN: 978-3-030-84383-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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