Methods and algorithms of the analytical platform for analyzing the labor market and the compliance of the higher education system with market needs

Abstract

The article discusses the methods and algorithms that underlie the analytical platform for automated monitoring and analysis of the labor market in the Russian Federation, as well as the analysis of the higher education system's compliance with the labor market's current needs. The study involved natural language processing methods and Big Data technologies. The general scheme corresponds to end-to-end processing -from data collection and storage, their transformation, analysis, and modeling, to visualization of results and decision-making. The analytical core of the system is a module for intellectual analysis of the texts of job advertisements in the labor market. The vacancies are collected from the most complete databases in Russia (namely HeadHunter, Work in Russia and SuperJob). Job descriptions of vacancies are matched with the official list of professions of the Ministry of Labor and Social Protection of Russia using semantic analysis based on neural models trained on large arrays of texts. Also, using semantic analysis, automated monitoring and intellectual analysis of the staffing needs of the all-Russian and regional labor markets are carried out according to the range of specialties of the university. Data gathering has been ongoing from 2015 up to now.

‘Insight unlocked’: Applying a collective intelligence approach to engage employers in informing local skills improvement planning

“…A similar approach was reported previously by Belov et al (2017 and 2022) but subsequently followed by few studies, concentrated on the Russian job market. In recent years the NLP models based in deep learning have become much more advanced than word2vec used by Belov.…”

“…The project is recognised as successful and has generated effective innovations, notably including the CISO model and the ability to gather and share regular contributions from participants automatically. This capability advances on the earlier work by Belov et al (2022). There were also process innovations to the EMC Quarterly Economic Survey (QES) which all Chambers are required to complete.…”

“…Few previous studies address the application of AI methods to local labour market analysis, but interestingly two were found in the Russian context. Belov et al (2022) demonstrated the use of methods and algorithms in an analytical platform for automated monitoring and analysis of the regional and national labour market in the Russian Federation, and linked analysis of the compliance of the higher education system's specialist training provision in meeting current labour market needs. The study involved natural language processing methods and Big Data technologies, using semantic analysis, automated monitoring and intellectual analysis on open-source datasets.…”

“…The region includes a major freight airport, international logistics businesses and a Freeport which present future opportunities. The ability to engage employers through gathering regular narrative inputs from them is a distinctive feature, which takes the project beyond the earlier work by Belov et al (2022). This capability has value in its own right as a longitudinal survey method for use with businesses, and an App is being developed to exploit this.…”