Graph-based adaptive feature fusion neural network model for person-job fit

Abstract

Online recruitment services are rapidly transforming traditional hiring practices in the job market. Accurate person-job fit is crucial for intelligent recruitment. Previous studies on person-job fit fail to explore job seekers’ resume information from a multi-perspective approach, and neglect the sustainable learning of resume features. To address this, the present paper proposes a Graph-based Person-Job Fit Neural Network Fusion (GPJFNNF) model. Specifically, the model first generates local semantic representations of job requirements and resume text using the BERT model. Next, a graph structure is constructed based on historical successful recruitment records, and the constructed resume graph is input into a graph neural network to obtain a global semantic representation of the resume. Finally, the adaptive feature fusion mechanism is used to fuse the local and global semantics of the resume, and the final semantic representation of the resume, along with the semantic representation of the job requirements, being input into the person-job fit layer. Experimental results demonstrate that the proposed model achieves 94.63%, 94.15%, 95.04%, and 94.59% in the person-job fit task in terms of accuracy, precision, recall, and F1, respectively, significantly outperforming state-of-the-art baselines.