RESUME RANKING SYSTEM USING MACHINE LEARNING & NLP

RESUME RANKING SYSTEM USING MACHINE LEARNING & NLP

Prof. . C.S. Wagh

 Harmanpreet Singh2, Ketan Mishra3, Parameswar Das4, Anupam Sagar5

Computer Engineering Department, ISBM College of Engineering Nande, Pune-412115, India  Savitribai Phule Pune University

Abstract: Selecting desirable applicants for an organization is one of the major challenges in human resource management. Through e-recruitment, candidates can now be represented on the web under a wider range of possibilities. This manual screening process may hinder the team's efforts to find the right candidate at the right time. The difficult screening process may be significantly streamlined by using an automated system for screening and ranking applicants. To accommodate the identified intelligence in hiring patterns, machine learning would be applied to understand the intelligence behind them. Using ML (Machine Learning) to rank the resumes with the given job requirements to match the best comparable candidates which is the most important and crucial task for any company to hire an ideal candidate for their job role. To classify resumes based on their respective categories, NLP (Natural Language Processing) and Machine Learning (ML) are used, while MaLSTM model(Siamese networks + LSTM with Manhattan distance) is used to rank candidates based on their resume's similarity to the job description.