Job Market

Computational job market analysis uses natural language processing (NLP) and machine learning to extract insights from job postings and resumes, aiming to improve job matching, skill forecasting, and fairness in hiring. Current research focuses on developing and benchmarking models, including large language models (LLMs) and traditional methods like named entity recognition (NER), to accurately identify skills, occupations, and potential biases in job descriptions. These advancements have significant implications for both researchers, providing improved datasets and evaluation frameworks, and practitioners, enabling more efficient recruitment processes and fairer hiring practices.