Portrait Technology in Campus Recruitment





practice campus recruitment, talent portrait, fuzzy c-means, general regression neural network


One limitation of campus recruitment is the ability of recruiters to quickly and accurately evaluate the comprehensive quality of students , resulting in the low success rate of signing, talent misjudgment, unreasonable post arrangement after successful signing and other problems. The application of traditional scientific research with small sample data based on statistics has gradually become difficult. This paper attempts to use artificial intelligence, big data and other technical means to develop intelligent solutions for campus recruitment scene. Starting from the problem, the researchers used clustering and neural network algorithms to realize the labeling of student behavior data, create the subjective and objective labeling system of students, and create the talent portrait suitable for campus recruitment. Research results of this paper shows such concerns can be effectively handled using talent portrait technology.


Received: 20 March 2023 | Revised: 10 April 2023 | Accepted: 24 April 2023


Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.


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Author Biography

Huang Yu, School of Advanced Studies, Saint Louis University, Philippines

Huang Yu (1981-) was born in Sichuan Province of China. He is currently a PhD candidate of the Graduate school of Saint Louis University in Philippines, an associate professor of Mianyang normal university in China, and a member of China Computer Federation (CCF). His main research area is in machine learning (ML) and deep learning algorithms and natural language processing.




How to Cite

Yu, H., & Mercado, C. (2023). Portrait Technology in Campus Recruitment. Artificial Intelligence and Applications. https://doi.org/10.47852/bonviewAIA3202873



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