Summary of A Cross-view Hierarchical Graph Learning Hypernetwork For Skill Demand-supply Joint Prediction, by Wenshuo Chao et al.
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
by Wenshuo Chao, Zhaopeng Qiu, Likang Wu, Zhuoning Guo, Zhi Zheng, Hengshu Zhu, Hao Liu
First submitted to arxiv on: 31 Jan 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 The proposed Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework is a novel approach for joint skill demand-supply prediction, addressing the limitations of existing methods that rely on domain-expert knowledge or simplified time series forecasting. The CHGH framework consists of an encoder-decoder network with three modules: cross-view graph encoding to capture interconnection between skill demand and supply, hierarchical graph encoding to model co-evolution of skills from a cluster-wise perspective, and conditional hyper-decoding for joint prediction by incorporating historical demand-supply gaps. Experimental results on three real-world datasets demonstrate the superiority of CHGH over seven baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict how much employees and employers need certain skills in the future. Right now, people are trying two different methods: one that relies on experts knowing what’s important, and another that tries to forecast by looking at past trends. But these approaches don’t take into account all the complex relationships between different skills or how demand and supply can change together. The new method, called CHGH, is a special kind of computer program that combines three parts: one that looks at how skills are connected, one that looks at how skills cluster together, and one that uses past information to make predictions. It works really well on real-world data and could be useful for people trying to stay ahead in the job market. |
Keywords
* Artificial intelligence * Encoder decoder * Time series
