RECOMMENDATION SYSTEM FOR APPLICANTS BASED ON A REGRESSION MODEL OF SALARY ANALYSIS IN THE IT INDUSTRY

Abstract

Recommender systems are actively developing in the field of education, helping applicants choose majors based on data analysis. Modern systems use artificial intelligence, machine learning, and big data analytics to provide personalized recommendations, allowing applicants to receive advice based on current job market trends and their own preferences.

The relevance of developing a recommendation system for applicants is due to the rapid development of the IT industry and constant changes in the labor market. Modern technologies and new specializations are emerging rapidly, and the level of salaries varies significantly depending on the demand for specialists. A recommendation system that analyzes real data on salaries in IT companies will help eliminate this information barrier and promote a more informed choice of curriculum.

The object of the study is the process of choosing an educational program by an applicant, which determines his future professional prospects. The subject of the study is the use of regression models and recommendation systems for predicting salaries and selecting optimal IT programs, which will contribute to a more rational choice of educational direction in accordance with the requirements of the labor market.

The scientific novelty of the proposed approach lies in the integration of regression salary forecasting with a recommendation system for selecting educational programs. The practical value of the project under development lies in its ability to help applicants, career counselors, and educational institutions make informed decisions about choosing an educational program. The system contributes to increasing the level of employment of graduates and their satisfaction with professional development, ensuring that the education obtained meets the current demands of the labor market.