IMPLEMENTATION OF NLP-BASED RECOMMENDATION SYSTEM IN VACANCY ANALYSIS

Abstract

The introduction of an NLP-based recommendation system in job analysis is a significant progress. This technology provides personalized recommendations based on market dynamics, which makes the job search process easier and more efficient for each user The purpose of the work is to develop and prepare for the practical implementation of a recommendation system based on natural language processing (NLP) methods for analyzing job openings and providing relevant job offers to users. The main objective is to increase the efficiency of job search and improve the match between the skills and requirements of candidates and employers. The methodology is an integrated approach to creating a job recommendation system that combines NLP methods with traditional collaborative and content filtering algorithms. At the preparation stage, data on vacancies and candidates’ resumes are collected and cleaned. Next, NLP techniques such as tokenization, feature extraction, named entity recognition, and topic modeling are applied to identify key skills, requirements, and contextual information. This data is used to create vector representations of jobs and resumes that serve as the basis for filtering algorithms. The proposed hybrid recommender system combines the results of content-based and collaborative filtering to provide personalized recommendations The scientific novelty. An integrated approach to job analysis that combines natural language processing, machine learning, and recommender systems is proposed. Unlike traditional search engines, this system provides personalised recommendations to candidates based on a meaningful analysis of their profiles and vacancies. The system improves the efficiency of recruitment by automating the process of matching candidates and vacancies. Conclusions. The results of the experimental studies have demonstrated the prospects of the proposed approach to creating a job recommendation system based on the integration of natural language processing (NLP) methods and traditional filtering algorithms. the results obtained demonstrate its significant potential for scaling the system to ensure its effective operation in real-world use.