Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
This addresses the problem of limited data for researchers and practitioners in workforce planning, though it is incremental as it focuses on dataset creation rather than a novel forecasting method.
The authors tackled the lack of comprehensive datasets for job skill demand forecasting by creating Job-SDF, a dataset based on 10.35 million job ads from China (2021-2023) covering 2,324 skills across 521 companies, and benchmarked various models to provide new insights.
In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on 10.35 million public job advertisements collected from major online recruitment platforms in China between 2021 and 2023, this dataset encompasses monthly recruitment demand for 2,324 types of skills across 521 companies. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://github.com/Job-SDF/benchmark.