Labor Migration Modeling through Large-scale Job Query Data

TL;DR

This paper presents DHG-SIL, a deep learning framework leveraging large-scale job query data to model labor migration with high temporal and regional resolution, outperforming traditional survey-based methods.

Contribution

The study introduces a novel deep learning framework, DHG-SIL, that uses large-scale job query data and interpretable variables to accurately model labor migration patterns.

Findings

DHG-SIL outperforms existing models on real-world datasets.

The framework effectively captures cross-city and sequential migration dependencies.

Deployment of DHG-SIL improved city talent attraction reporting.

Abstract

Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection. However, existing studies on labor migration largely rely on limited survey data with statistical methods, which fail to deliver timely and fine-grained insights for time-varying regional trends. To this end, we propose a deep learning-based spatial-temporal labor migration analysis framework, DHG-SIL, by leveraging large-scale job query data. Specifically, we first acquire labor migration intention as a proxy of labor migration via job queries from one of the world's largest search engines. Then, a Disprepant Homophily co-preserved Graph Convolutional Network (DH-GCN) and an interpretable temporal module are respectively proposed to capture cross-city and sequential labor migration dependencies. Besides, we introduce four interpretable variables to quantify city migration properties, which are co-optimized with city representations via tailor-designed contrastive losses. Extensive experiments on three real-world datasets demonstrate the superiority of our DHG-SIL. Notably, DHG-SIL has been deployed as a core component of a cooperative partner's intelligent human resource system, and the system supported a series of city talent attraction reports.