<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to solve the social phenomenon of difficult employment of college students, this paper constructs a college employment recommendation platform based on an improved collaborative filtering algorithm. Firstly, the framework of the employment recommendation system is designed for the user use case, system architecture, system function module, database and user portrait of the platform, respectively. The functional modules are divided into six module parts: campus recruitment management, internship management, employment information management, employment affairs management, employment analysis management, and employment guidance management. Next, the platform is analyzed to combine the recommendation algorithm of collaborative filtering algorithm and clustering technology based on users and items to realize the recommendation of employment information for college students. Then the employment platform is used to get the high stickiness of users in the employment of college students, which can retain about 62% of the number of people, and even 5% of the number of people are particularly fond of the platform. With the increase in recommendation times, the recommendation accuracy increased from 22% to 83%, and the accuracy increased by about 61%, and the comparison with the traditional employment form is much higher than the traditional method in both accuracy and recall rate by about 30%. It is concluded that using the college employment recommendation platform can realize the precise employment of college students.</p>
</abstract>

In order to solve the social phenomenon of difficult employment of college students, this paper constructs a college employment recommendation platform based on an improved collaborative filtering algorithm. Firstly, the framework of the employment recommendation system is designed for the user use case, system architecture, system function module, database and user portrait of the platform, respectively. The functional modules are divided into six module parts: campus recruitment management, internship management, employment information management, employment affairs management, employment analysis management, and employment guidance management. Next, the platform is analyzed to combine the recommendation algorithm of collaborative filtering algorithm and clustering technology based on users and items to realize the recommendation of employment information for college students. Then the employment platform is used to get the high stickiness of users in the employment of college students, which can retain about 62% of the number of people, and even 5% of the number of people are particularly fond of the platform. With the increase in recommendation times, the recommendation accuracy increased from 22% to 83%, and the accuracy increased by about 61%, and the comparison with the traditional employment form is much higher than the traditional method in both accuracy and recall rate by about 30%. It is concluded that using the college employment recommendation platform can realize the precise employment of college students.

Analysis of using big data to achieve precise employment of college students

Zhengzhou University of Industrial Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Analysis of using big data to achieve precise employment of college students"}

In order to solve the social phenomenon of difficult employment of college students, this paper constructs a college employment recommendation platform based...

Analysis of using big data to achieve precise employment of college students

Online veröffentlicht: 04. Okt. 2023

Seitenbereich: -

Eingereicht: 20. Okt. 2022

Akzeptiert: 14. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00491

Schlüsselwörter
Collaborative filtering, Employment recommendation platform, Clustering technology, Precise employment

© 2023 Peng Su, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of using big data to achieve precise employment of college students

Online veröffentlicht: 04. Okt. 2023

Seitenbereich: -

Eingereicht: 20. Okt. 2022

Akzeptiert: 14. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00491

SchlüsselwörterCollaborative filtering, Employment recommendation platform, Clustering technology, Precise employment

© 2023 Peng Su, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Collaborative filtering, Employment recommendation platform, Clustering technology, Precise employment