I. L. Sizova. Features of Recruitment: Intelligent Text Analysis of Resume and Vacancies

https://doi.org/10.15507/2413-1407.129.033.202502.271-293
EDN: https://elibrary.ru/sudwcp
УДК / UDC 331.5-051

Abstract

Introduction. This study is devoted to the analysis of the peculiarities of personnel selection in the Russian labor market, with an emphasis on the problem of imbalance between the requirements of employers and the expectations of job seekers. The focus of attention is on the differences in the representation of the parties’ demands, which allow systematizing the staffing needs of the economy through the prism of competencies and job responsibilities. The relevance of the study is due to the low conjugality of interaction between the subjects of the labor market in the conditions of digitalization, which generates systemic barriers in job search and recruitment. Meanwhile, this topic is rarely the subject of sociological analysis and is insufficiently represented in the field.

Materials and Methods. An original methodology for analyzing labor market big data was developed, based on 5,347,805 texts of resumes and job postings collected from three largest Russian platforms for the period 2019–2024. Natural Language Processing (NLP) algorithms, including topic modeling and clustering, were used to process the data, enabling the creation of two hierarchical taxonomies: 55 competency parameters and 423 groups of job responsibilities. Statistical analysis included methods of descriptive statistics, correlation analysis, and time-series modeling.

Results. Empirical analysis revealed persistent information asymmetry between employers and job seekers. The parties demonstrate insufficient understanding of the functional purpose of key sections in job postings (competencies and job responsibilities). Job seekers tend to list more competency parameters than required by employers, indicating an exaggerated perception of market demand. A growing interest of employers in certain groups of job responsibilities is observed; however, job postings retain a generalized character, contrasting with the detailed specialization of experience in resumes.

Discussion and Conclusion. The degree of consistency of the requirements of the labor market subjects is analyzed with a focus on the current and future imbalances between the parameters of supply and demand. It is found that the labor market functions under conditions of bounded rationality. Job advertisements fulfill different functions depending on the interpretations of the parties. The parameters of competencies and job responsibilities in the texts of vacancy announcements and resumes do not coincide, which leads to protracted and inefficient processes of candidate selection. The results of the study can be used to develop recommendations for optimizing texts related to job search and personnel selection.

Keywords: job vacancies, resumes, recruitment, competencies, job responsibilities, machine learning algorithms, taxonomy, HR technologies, natural language processing (NLP)

Conflict of interest. The author declares no conflict of interest.

Funding. The article was prepared with the support of the Russian Science Foundation, project No. 23-28-00011 “Deficit of Workers’ Competencies in Open Remote Labor Markets Under Conditions of Socio-­Economic Uncertainty”.

For citation: Sizova I.L. Features of Recruitment: Intelligent Text Analysis of Resume and Vacancies. Russian Journal of Regional Studies. 2025;33(2):271–293. https://doi.org/10.15507/2413-1407.129.033.202502.271-293

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About the author:

Irina L. Sizova, Dr.Sci. (Sociol.), Professor, Chair of Sociology, St. Peterburg State University (7–9 Universitetskaya Emb., St. Petersburg 199034, Russian Federation), ORCID: https://orcid.org/0000-0001-5656-0670, Researcher ID: AAJ-7300-2020, Scopus ID: 56195417000, SPIN-code: 7517-3320, sizovai@mail.ru

Availability of data and materials. The datasets used and/or analyzed during the current study are available from the author on reasonable request.

The author have read and approved the final manuscript.

Submitted 11.11.2024; revised 14.01.2025; accepted 25.01.2025.