Fine-Grained Semantics-Enhanced Graph Neural Network Model for Person-Job Fit
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 depende...
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| Huvudupphovsmän: | , , , , , , , , , |
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| Materialtyp: | Artikel |
| Språk: | engelska |
| Publicerad: |
MDPI AG
2025-06-01
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| Serie: | Entropy |
| Ämnen: | |
| Länkar: | https://www.mdpi.com/1099-4300/27/7/703 |
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| Sammanfattning: | 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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| ISSN: | 1099-4300 |