The term "resume matching" refers to the practice of comparing an applicant's written work (CV) or resume with specific job qualifications or job descriptions. The purpose of this process is to determine how well a candidate's relevant traits, such as their abilities, qualifications, and experience, match the requirements of the job. Students enrolled in these types of courses often learn how to analyse job postings for essential qualifications and then craft their resumes to emphasise those areas. Human resources (HR) professionals, on the other hand, have the education and experience to sift through stacks of resumes for the best possible fit with their company. It is common practice to use an automated system to compare resume content with job postings, and then to rank or score the results based on how similar the two sets of words are. With a vast pool of candidates and detailed job postings, though, this procedure can quickly grow tedious. Using vector search techniques to align job applicants' resumes with suitable job descriptions, this research proposes a novel strategy for enhancing the job matching process. Employers and job-seekers alike stand to gain from the suggested system's efforts to improve the precision and efficacy of employment referrals. In this article, we offer a dataset that includes software developer resumes culled from an open Telegram channel that is specifically for Israeli high-tech job seekers. In addition, we offer an NLP-based approach to resume matching that makes use of neural representations of phrases, keywords, and named entities to achieve first-rate outcomes.

We show that our method outperforms the top algorithm for matching resumes with job openings by evaluating it with both human and automated annotations.