Fine-Grained Semantics-Enhanced Graph Neural Network Model for Person-Job Fit

TL;DR

This paper introduces a new AI model that improves matching between job seekers and job postings by better understanding the detailed meaning of their resumes and job descriptions.

Contribution

The novel FSEGNN-PJF framework enhances person-job fit by using fine-grained semantics and graph neural networks to capture textual dependencies and reduce noise.

Findings

FSEGNN-PJF outperforms existing methods in matching resumes with job descriptions.

The model effectively captures structural semantics and reduces noise in textual data.

Experiments on real-world datasets show the framework's robustness and effectiveness.

Abstract

Online recruitment platforms are transforming talent acquisition paradigms, where a precise person-job fit plays a pivotal role in intelligent recruitment systems. However, current methodologies predominantly rely on coarse-grained semantic analysis, failing to address the textual structural dependencies and noise inherent in resumes and job descriptions. To bridge this gap, the novel fine-grained semantics-enhanced graph neural network for person-job fit (FSEGNN-PJF) framework is proposed. First, graph topologies are constructed by modeling word co-occurrence relationships through pointwise mutual information and sliding windows, followed by graph attention networks to learn graph structural semantics. Second, to mitigate textual noise and focus on critical features, a differential transformer and self-attention mechanism are introduced to semantically encode resumes and job requirements. Then, a novel fine-grained semantic matching strategy is designed, using the enhanced feature fusion strategy to fuse the semantic features of resumes and job positions. Extensive experiments on real-world recruitment datasets demonstrate the effectiveness and robustness of FSEGNN-PJF.

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