Research on personalised recommendation algorithm for college students’ employment

Li, Wenle

doi:10.2478/amns.2021.2.00234

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…To address this, Shi [35] proposes to integrate key technologies, such as graduate feature technology, similarity algorithms, and neighbor selection mechanisms, to establish a graduate employment recommendation system, with the aim of broadening the scope of recommendation results. Li [36] combines recommendation and machine learning algorithms to match and optimize job options for college students based on their major types, interests, specialties, preferred employment areas, and other job-related characteristics. This integration improves the diversity and satisfaction of job recommendations simultaneously.…”

Section: The Application Of Job Recommendation In the Field Of Collegesmentioning

confidence: 99%

“…In employment, the degree of professional alignment between job seekers and job positions is a crucial factor influencing job search outcomes. With the growing gap between job availability and the demand for skilled workers, employers are emphasizing professional compatibility between job roles and candidates [36]. Rational choice theory posits that individuals are rational decision-makers who weigh the costs and potential benefits when arriving at decisions [40].…”

Section: Ranking Stagementioning

confidence: 99%

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A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

Yao,

Xu,

Gao

2023

Applied Sciences

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With the surge in college graduate numbers, a disparity has emerged where the supply of jobs falls short of demand, intensifying employment pressures annually. College graduates, due to their lack of historical employment data compared with job seekers in the broader society, encounter a ‘cold start’ issue in the job recommendation process. Additionally, the nature of job recommendations, which differs fundamentally from unilateral recommendations, requires consideration of reciprocity between both parties involved. This article introduces a new approach to job recommendations using college graduates as the object of study. In the screening stage, a semantic keyword iterative algorithm is applied to compute the similarity between the resume and recruitment texts. This algorithm enhances the intersectionality of keywords in the calculation process, maximizing the utilization of resume information to enhance the accuracy of text similarity calculations. The ranking phase utilizes in-school data to build a social network between college graduates and graduated students and solves the system’s cold-start problem using the social network to recommend jobs for college graduates where graduated students are employed. We introduce a dual-dimensional matching approach that incorporates both specialty and salary, building upon the amalgamated semantic keyword iterative algorithm and the social network job recommendation method, to enhance the reciprocity of job recommendations. The job recommendation method introduced herein outperforms other methods in terms of the average satisfaction rate (AR) and normalized discounted cumulative gain (NDCG), thereby confirming its superior ability to meet the job-seeking preferences of graduates and the recruitment criteria of employers. This job recommendation method offers effective assistance to graduates lacking employment experience and historical employment data, facilitating their search for more suitable job opportunities.

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Section: The Application Of Job Recommendation In the Field Of Collegesmentioning

confidence: 99%

Section: Ranking Stagementioning

confidence: 99%

A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

Yao,

Xu,

Gao

2023

Applied Sciences

View full text Add to dashboard Cite

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Research on College Students’ Career Planning and Employment Guidance Strategies Assisted by Artificial Intelligence

Feng

2024

Applied Mathematics and Nonlinear Sciences

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The number of college graduates is increasing year by year, and college students’ career planning and employment have always been a high concern of the community. The article describes the specific role of artificial intelligence technology in assisting college students’ career planning and employment guidance and combines WEB technology to construct a college student’s career planning and employment guidance service platform. The information entropy is used to construct the feature importance set of student employment data, and the simple Bayesian algorithm is used to realize the construction of the classification model of student employment service data in combination with the feature importance. On the basis of considering the selection of students’ employment information, the time decay factor is introduced to improve the personalized recommendation algorithm, and the cosine similarity between students’ employment positions is calculated so as to realize the generation of students’ employment service information recommendation. The area of the classification ROC curve of the plain Bayesian model is more than 95%, the classification time for student employment service data is only 5.62ms, and the F1 value of the employment service information recommendation result is 6.03% higher than that of the traditional method. The college students’ career planning and employment guidance service platform was only satisfactory for 31.77% of the students, with the highest satisfaction score of 3.69 for getting information. Relying on artificial intelligence technology can help students make personalized recommendations for employment service information, which can improve the level of career planning and guidance for college students.

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Research on personalised recommendation algorithm for college students’ employment

Cited by 2 publications

References 22 publications

A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

Research on College Students’ Career Planning and Employment Guidance Strategies Assisted by Artificial Intelligence

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