Research on personalized recommendation from the perspective of staff-position matching

Chao Deng

doi:10.1117/12.2680522

27 June 2023 Research on personalized recommendation from the perspective of staff-position matching

Chao Deng

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127053F (2023) https://doi.org/10.1117/12.2680522
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

In the context of the COVID-19 epidemic, the mutual selection between job seekers and enterprises mostly relies on online publishing of recruitment information and the delivering resumes online. How can job seekers obtain suitable employment information quickly and effectively, and how companies can accurately recruit people who are suitable for their positions, these are very challenging problems. In this paper, taking personnel-job matching as a theoretical guide, based on the latent semantic model, the correlation model between the job seeker and the applicant's recruitment requirements was built, after comparing the similarity values of similar companies and job seekers. Realized the purpose of recommending data information with a high degree of matching to job seekers and enterprises.

Citation Download Citation

Chao Deng "Research on personalized recommendation from the perspective of staff-position matching", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127053F (27 June 2023); https://doi.org/10.1117/12.2680522

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