Summary of Job-sdf: a Multi-granularity Dataset For Job Skill Demand Forecasting and Benchmarking, by Xi Chen et al.
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
by Xi Chen, Chuan Qin, Chuyu Fang, Chao Wang, Chen Zhu, Fuzhen Zhuang, Hengshu Zhu, Hui Xiong
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the crucial issue of skill demand forecasting in a rapidly evolving job market. By anticipating changes in workforce skills, policymakers and businesses can adapt to ensure alignment with market needs, enhancing productivity and competitiveness. The authors highlight the challenge posed by the lack of comprehensive datasets, which hinders research and advances in this field. To bridge this gap, they present Job-SDF, a dataset designed for training and benchmarking job-skill demand forecasting models. This dataset includes 10.35 million public job advertisements from major online recruitment platforms in China between 2021 and 2023, covering 2,324 skill types across 521 companies. The authors demonstrate the unique capabilities of this dataset by evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. They also benchmark a range of models on this dataset, assessing their performance in standard scenarios, low-value predictions, and structural breaks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting what skills people will need for jobs in the future. This helps governments and companies prepare for changes in the job market, so they can match workers with the right training to fill open positions. Right now, there isn’t a good way to gather data on this topic, which makes it hard for researchers to study. To fix this, the authors created a big dataset called Job-SDF that contains information from millions of job postings in China. This dataset is special because it lets researchers test different models for predicting skill demand at different levels, like individual jobs or companies. The authors also tested these models on their dataset and found some interesting results. |
Keywords
* Artificial intelligence * Alignment
