Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model
In the context of the accelerating new technological revolution and industrial transformation, the issue of talent supply and demand matching has become increasingly urgent. Precise matching talent supply and demand is a critical factor in expediting the implementation of technological innovations....
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2536 |
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| author | Kunping Li Jianhua Liu Cunbo Zhuang |
| author_facet | Kunping Li Jianhua Liu Cunbo Zhuang |
| author_sort | Kunping Li |
| collection | DOAJ |
| description | In the context of the accelerating new technological revolution and industrial transformation, the issue of talent supply and demand matching has become increasingly urgent. Precise matching talent supply and demand is a critical factor in expediting the implementation of technological innovations. However, traditional methods relying on interpersonal networks for talent ability collection, demand transmission, and matching suffer from inefficiency and are often influenced by the subjective intentions of intermediaries, posing significant limitations. To address this challenge, we propose a novel approach named TSDM for talent supply and demand matching. TSDM leverages prompt learning with pre-trained large language models to extract detailed expressions of talent ability and demand from unstructured documents while utilizing the powerful text comprehension capabilities of pre-trained models for feature embedding. Furthermore, TSDM employs talent-specific and demand-specific encoding networks to perform deep learning on talent and demand features, capturing their comprehensive representations. In a series of comparative experiments, we validated the effectiveness of the proposed model. The results demonstrate that TSDM significantly enhances the accuracy of talent supply and demand matching, offering a promising approach to optimize human resource allocation. |
| format | Article |
| id | doaj-art-b3f6e8dccdd149259ee851273bbc97b7 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b3f6e8dccdd149259ee851273bbc97b72025-08-20T02:57:41ZengMDPI AGApplied Sciences2076-34172025-02-01155253610.3390/app15052536Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language ModelKunping Li0Jianhua Liu1Cunbo Zhuang2Laboratory of Digital Manufacturing, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaLaboratory of Digital Manufacturing, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaLaboratory of Digital Manufacturing, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaIn the context of the accelerating new technological revolution and industrial transformation, the issue of talent supply and demand matching has become increasingly urgent. Precise matching talent supply and demand is a critical factor in expediting the implementation of technological innovations. However, traditional methods relying on interpersonal networks for talent ability collection, demand transmission, and matching suffer from inefficiency and are often influenced by the subjective intentions of intermediaries, posing significant limitations. To address this challenge, we propose a novel approach named TSDM for talent supply and demand matching. TSDM leverages prompt learning with pre-trained large language models to extract detailed expressions of talent ability and demand from unstructured documents while utilizing the powerful text comprehension capabilities of pre-trained models for feature embedding. Furthermore, TSDM employs talent-specific and demand-specific encoding networks to perform deep learning on talent and demand features, capturing their comprehensive representations. In a series of comparative experiments, we validated the effectiveness of the proposed model. The results demonstrate that TSDM significantly enhances the accuracy of talent supply and demand matching, offering a promising approach to optimize human resource allocation.https://www.mdpi.com/2076-3417/15/5/2536talent supply and demand matchingprompt learningpre-trained language modeldeep learning |
| spellingShingle | Kunping Li Jianhua Liu Cunbo Zhuang Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model Applied Sciences talent supply and demand matching prompt learning pre-trained language model deep learning |
| title | Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model |
| title_full | Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model |
| title_fullStr | Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model |
| title_full_unstemmed | Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model |
| title_short | Talent Supply and Demand Matching Based on Prompt Learning and the Pre-Trained Language Model |
| title_sort | talent supply and demand matching based on prompt learning and the pre trained language model |
| topic | talent supply and demand matching prompt learning pre-trained language model deep learning |
| url | https://www.mdpi.com/2076-3417/15/5/2536 |
| work_keys_str_mv | AT kunpingli talentsupplyanddemandmatchingbasedonpromptlearningandthepretrainedlanguagemodel AT jianhualiu talentsupplyanddemandmatchingbasedonpromptlearningandthepretrainedlanguagemodel AT cunbozhuang talentsupplyanddemandmatchingbasedonpromptlearningandthepretrainedlanguagemodel |