Enhanced Algorithmic Job Matching based on a Comprehensive Candidate Profile using NLP and Machine Learning
Applied Data Science
2022 IEEE Eighth International Conference on Big Data Computing Service and Applications (BigDataService)
We propose to aid the hiring process by automatic profiling of a candidate’s social media presence and using it in conjunction with their resume and other information to suggest an employability score and an emotional intelligence indicator. Algorithmic hiring usually supports the hiring company and does not factor in social media presence of the candidate. The work we propose in this paper can also be used by the job seekers to evaluate their chances and uses a comprehensive profile of the candidate. Our approach uses social media profiling using APIs and web crawlers, evaluating soft skills, shortlisting candidates based on keywords, skill-set, and educational requirements and suggesting a match using Machine Learning and NLP techniques. The resulting application provides a faster, accurate, efficient, and relatively bias-free recruitment process to the companies and to the job seekers.
Machine Learning, Classification, Recruitment, Social Media, Natural Language Processing
Vishnu S. Pendyala, Nishtha Atrey, Tina Aggarwal, and Saumya Goyal. "Enhanced Algorithmic Job Matching based on a Comprehensive Candidate Profile using NLP and Machine Learning" 2022 IEEE Eighth International Conference on Big Data Computing Service and Applications (BigDataService) (2022). https://doi.org/10.1109/BigDataService55688.2022.00040