User Profile: Research on Employment Recommendation and Guidance for College Graduates After Graduation

Authors

  • Jing Luo Chengdu Polytechnic, Chengdu, Sichuan 610000, China

Abstract

As the number of college graduates continues to increase, the employment pressure has also increased. Based on user profiling, this paper designed a post-graduation employment recommendation and guidance method for college students. The method extracted features such as work experience and salary from college students’ resumes and job information, used the K-means++ algorithm to cluster the resumes and job information respectively to obtain user and job profiles, and matched the resume text with the job text to obtain the recommendation list. Student data from a college in Sichuan Province, China, were selected for experiments. It was found that the K-means++ algorithm outperformed the traditional algorithm in clustering, with
a Calinski-Harabasz index of 503 and a silhouette coefficient of 0.812. In terms of employment recommendation, when the number of recommended positions was 20, the recommendation effect of this method was the best, with an F1 value of 0.655, outperforming the traditional collaborative filtering algorithm. The results demonstrate the reliability of the proposed method in employment recommendation and guidance, and it can be applied to the employment of post-graduates.

Keywords: user profiling, college student, employment recommendation, k-means++

Cite As

J. Luo, "User Profile: Research on Employment Recommendation and Guidance for College Graduates After Graduation",
Engineering Intelligent Systems, vol. 34 no. 5, pp. 697-702, 2026.






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Published

2026-09-01

Issue

Vol. 34 No. 5 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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