Occupation Similarity

Occupation similarity research aims to quantify the relationships between different jobs, informing career guidance, workforce planning, and the development of intelligent systems. Current research focuses on developing computational methods, including large language models (LLMs) and graph neural networks, to analyze textual descriptions of occupations, job transitions, and associated skills, often leveraging large datasets of job postings, resumes, and census data. These advancements enable more accurate predictions of career trajectories and improved understanding of occupational structures, with implications for both economic modeling and the design of personalized digital assistants. Furthermore, the field is actively addressing biases in existing data and models to ensure fairness and equity in applications.