Learning Multi-Graph Neural Network for Data-Driven Job Skill Prediction

Specifying an appropriate skill set for a job position is critical for talent recruitment. However, it is often more difficult than people think since it needs a great understanding of the role of the position, related technologies, and even the global situation of the job market. To this end, we propose to learn the mapping between the job position description and its required skills in a data-driven manner. This task is challenging due to the complex mapping relationships between job descriptions and skills, which is caused by complex influencing factors. In this paper, we propose a novel Multi-Graph Neural Network based Skill Prediction model (MGNSP) to make skill prediction by learning effective deep semantics matching of job positions and skills. Specifically, to capture the complex heterogeneous relations among the job positions, skills, and meta information, we devise a joint learning approach of graph neural networks for multiple information networks, which are J-Net, S-Net and JS-Net, respectively. After jointly learning complementary semantics of job positions and skills with three multi-layer graph neural networks from these information networks, the skills are predicted by learning to match their representations. Extensive experimental results on a real-world dataset validate the effectiveness of our model.