CURRICULUM VITAE ANALYSIS USING MACHINE LEARNING ALGORITHM

We describe a system called CV analysis machine, that processes a set of given free-form textual resumes (in English for IT domain), creates a standardized profile for each candidate, and for a given job description, identifies a ranked shortlist of k candidates, along with a matching score for each. The resume scoring function is hand-crafted, hierarchical, and uses domain-knowledge from recruitment experts. We describe a simple neural- network system that automatically learns some weights used in the scoring function, based on feedback about whether the candidate has their grades on each portion and calculate the overall grade of each candidate and find all grades at a time. Companies often receive thousands of resumes for each job posting and employ dedicated screeners to short list qualified applicants. In this paper, we present PROSPECT, a decision support tool to help these screeners shortlist resumes efficiently. Prospect mines resumes to extract salient aspects of candidate profiles like skills, experience in each skill, education details and experience. Extracted information is presented in the form of facets to aid recruiters in the task of screening. We also employ Information Retrieval techniques to rank all applicants for a given job opening. In our experiments we show that extracted information improves our ranking by 30% there by making screening tasks simpler and more efficient.