The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market. Existing efforts in this area either rely on domain-expert knowledge or regarding skill evolution as a simplified time series forecasting problem. However, both approaches overlook the sophisticated relationships among different skills and the inner-connection between skill demand and supply variations. In this paper, we propose a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework for joint skill demand-supply prediction. Specifically, CHGH is an encoder-decoder network consisting of i) a cross-view graph encoder to capture the interconnection between skill demand and supply, ii) a hierarchical graph encoder to model the co-evolution of skills from a cluster-wise perspective, and iii) a conditional hyper-decoder to jointly predict demand and supply variations by incorporating historical demand-supply gaps. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed framework compared to seven baselines and the effectiveness of the three modules.

翻译：技术与产业的快速演变导致了动态技能需求变化，使得员工和雇主预测此类变化以在劳动力市场中保持竞争优势变得至关重要。现有相关研究要么依赖领域专家知识，要么将技能演化简化为时间序列预测问题。然而，这两种方法都忽略了不同技能之间的复杂关系以及技能需求与供给变化的内在关联。本文提出一种跨视图层次图学习超网络（CHGH）框架，用于技能供需联合预测。具体而言，CHGH是一种编码器-解码器网络，包含：i) 跨视图图编码器，用于捕捉技能需求与供给之间的相互连接；ii) 层次图编码器，从聚类视角建模技能的协同演化；iii) 条件超解码器，通过融合历史供需差距实现供需变化的联合预测。在三个真实数据集上的大量实验表明，相较于七个基线方法，该框架具有显著优越性，且三个模块均有效。