An intelligent system for job recommendation based on semantic analysis of candidate's resume

Abstrak

The contemporary job market presents significant obstacles to effectively aligning proficient candidates with pertinent employment prospects. The conventional methods of resume screening and job matching frequently require substantial manual effort and are susceptible to subjective biases, resulting in recruiting decisions that are frequently suboptimal. The present study proposes the development of an intelligent job recommendation system that utilises semantic analysis of candidates' resumes and job descriptions sourced from several job portals. The objective of the proposed intelligent system is to enhance and streamline the recruiting process through the automated extraction and analysis of pertinent skills from resumes and job descriptions, utilising natural language processing (NLP) and machine learning (ML) techniques. In addition, web scraping techniques were used to collect job advertisements from several job portals. The developed model exhibits the ability to recommend the most suitable job prospects by computing similarity metrics, such as Euclidean distance, between skill clusters identified in a job advertisement and a specified candidate's resume. The implemented model achieves an accuracy rate of 98.92%. It is anticipated that the integration of an intelligent job recommendation system will augment the recruitment procedure for both job seekers and employers.