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<p>With the popularization of higher education, the competition in the employment market of college students is becoming increasingly intense. To enhance the employment efficiency and satisfaction of college students. The study first analyzes college students’ employment unit selection, attribute preference, and location preference through an employment recommendation algorithm. The collaborative filtering algorithm is utilized to complete personalized modeling and output the final recommendation results based on the acquired employment preferences and relevant data collection. Finally, the gradient descent method is used to evaluate the accuracy of college students’ employment recommendations. The results show that the overall educational requirements of enterprises for the three significant positions of short video production, account operation and anchor are not high, and the percentage of those with education of high school or below or master’s degree or above is meager, neither exceeding 3.5%. The personalized employment recommendation algorithm can provide scientific and reasonable guidance for graduates’ employment, with an accuracy rate of up to 50% and a recommendation list length of N=30. When α=0.75, the personalized employment recommendation algorithm can obtain better recommendation performance with smaller recommendation list length. This paper provides new solutions for college students’ employment and valuable references and lessons for research in related fields.</p>
</abstract>

With the popularization of higher education, the competition in the employment market of college students is becoming increasingly intense. To enhance the employment efficiency and satisfaction of college students. The study first analyzes college students’ employment unit selection, attribute preference, and location preference through an employment recommendation algorithm. The collaborative filtering algorithm is utilized to complete personalized modeling and output the final recommendation results based on the acquired employment preferences and relevant data collection. Finally, the gradient descent method is used to evaluate the accuracy of college students’ employment recommendations. The results show that the overall educational requirements of enterprises for the three significant positions of short video production, account operation and anchor are not high, and the percentage of those with education of high school or below or master’s degree or above is meager, neither exceeding 3.5%. The personalized employment recommendation algorithm can provide scientific and reasonable guidance for graduates’ employment, with an accuracy rate of up to 50% and a recommendation list length of N=30. When α=0.75, the personalized employment recommendation algorithm can obtain better recommendation performance with smaller recommendation list length. This paper provides new solutions for college students’ employment and valuable references and lessons for research in related fields.

Implementation and Application of Employment Recommendation System for College Students with Personalized Preferences

Applied Mathematics and Nonlinear Sciences

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

With the popularization of higher education, the competition in the employment market of college students is becoming increasingly intense. To enhance the...

Implementation and Application of Employment Recommendation System for College Students with Personalized Preferences

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 03 apr 2024

Accettato: 14 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1033

Parole chiave
Collaborative filtering algorithm, Gradient descent method, Personalized sorting, College student employment

© 2024 Fang Guo et al., published by Sciendo

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

Implementation and Application of Employment Recommendation System for College Students with Personalized Preferences

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 03 apr 2024

Accettato: 14 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1033

Parole chiaveCollaborative filtering algorithm, Gradient descent method, Personalized sorting, College student employment

© 2024 Fang Guo et al., published by Sciendo

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

Parole chiave
Collaborative filtering algorithm, Gradient descent method, Personalized sorting, College student employment