Yao, J.; Xu, Y.; Gao, J. A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking. Appl. Sci.2023, 13, 12305.
Yao, J.; Xu, Y.; Gao, J. A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking. Appl. Sci. 2023, 13, 12305.
Yao, J.; Xu, Y.; Gao, J. A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking. Appl. Sci.2023, 13, 12305.
Yao, J.; Xu, Y.; Gao, J. A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking. Appl. Sci. 2023, 13, 12305.
Abstract
s: The lack of historical employment data for college graduates, the need to solve the system cold-start problem and the consideration of reciprocity of job recommendation in job recommendation, lead to low recommendation satisfaction and immature application of the existing job recommendation methods. The article presents a new approach to job recommendation 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 by using the social network to recommend jobs for college graduates where graduated students are employed. Building upon the amalgamation of the semantic keyword iterative algorithm and the social network job recommendation method outlined above, we introduce a dual-dimensional matching approach involving specialty and salary. This enhancement is designed to elevate the reciprocity of job recommendations. The analysis of the results indicates that the average satisfaction rate (AR) and normalized discounted cumulative gain (NDCG) values for the newly proposed job recommendation method surpass those of other methods, demonstrating its superior effectiveness. The method caters to the preferences of graduate job seekers, aligns with job recruitment requirements, and offers extensive job search assistance to a broad spectrum of graduates.
Keywords
job recommendation; semantic keyword matching; reciprocity; social networks; college graduates
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
