Portrait Technology in Campus Recruitment

Abstract

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.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.

Author Contribution Statement

Huang Yu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Funding acquisition. Cecilia Mercado: Conceptualization, Investigation, Resources, Writing - original draft, Writing - review & editing, Supervision, Project administration.