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Дата издания
23.12.2025На каком языке издана
ISSN
1998-6688Область исследования
Аннотация (краткое описание)
The research introduces a dual deep learning system which predicts salary ranges by processing job descriptions through BERT-based contextual embeddings and structured metadata integration. The proposed method utilizes more than 124,000 LinkedIn job postings to merge BERT-based contextual embeddings with structured information about location and industry and experience level and compensation type. The model uses multi-head attention to identify essential salary-related terms in job descriptions which results in better model interpretability and improved prediction accuracy. The model combines semantic embeddings with tabular data to create a multimodal representation which serves as input for supervised learning with an ordinal-aware loss function. The model achieves stable performance in salary classification across three categories through F1-scores between 0.82 and 0.84. The proposed model achieves excellent generalization capabilities for different sectors and job types while providing precise predictions and clear decision-making processes for salary benchmarking and recruitment analytics applications.DOI
10.55452/1998-6688-2025-22-4-168-177Тип публикаций
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Международные публикации ученых SU
Scopus
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2292-5716-1-SM.pdf