mlsanityData to 7 Oct 2026

Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking

arXiv 2406.11920 ↗First posted 17 Jun 2024cs.LG cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 33 of its 90 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

33 of 90 references.

References · 33

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21 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 2024Natural Language Processing for Human Resources: A Surveycited by 9
  2. 2024DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendationcited by 2
  3. 2026Forecasting Green Skill Demand in the Automotive Industry: Evidence from Online Job Postingscited by 0