A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs
In this study, an end-to-end person-to-job post data matching model is constructed, and the experiments for matching people with the actual recruitment data are conducted. First, the representation of the constructed knowledge in the low-dimensional space is described. Then, it is explained in the B...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6206288 |
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| _version_ | 1832565239112007680 |
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| author | Xiaowei Wang Zhenhong Jiang Lingxi Peng |
| author_facet | Xiaowei Wang Zhenhong Jiang Lingxi Peng |
| author_sort | Xiaowei Wang |
| collection | DOAJ |
| description | In this study, an end-to-end person-to-job post data matching model is constructed, and the experiments for matching people with the actual recruitment data are conducted. First, the representation of the constructed knowledge in the low-dimensional space is described. Then, it is explained in the Bidirectional Encoder Representations from Transformers (BERT) pretraining language model, which is introduced as the encoding model for textual information. The structure of the person-post matching model is explained in terms of the attention mechanism and its computational layers. Finally, the experiments based on the person-post matching model are compared with a variety of person-post matching methods in the actual recruitment dataset, and the experimental results are analyzed. |
| format | Article |
| id | doaj-art-6ede87d6efe64db5a689c56d4b45edf5 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-6ede87d6efe64db5a689c56d4b45edf52025-02-03T01:08:51ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/62062886206288A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term GraphsXiaowei Wang0Zhenhong Jiang1Lingxi Peng2School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaData Recovery Key Laboratory of Sichuan Province, Neijiang Normal University, Neijiang, Sichuan 641100, ChinaIn this study, an end-to-end person-to-job post data matching model is constructed, and the experiments for matching people with the actual recruitment data are conducted. First, the representation of the constructed knowledge in the low-dimensional space is described. Then, it is explained in the Bidirectional Encoder Representations from Transformers (BERT) pretraining language model, which is introduced as the encoding model for textual information. The structure of the person-post matching model is explained in terms of the attention mechanism and its computational layers. Finally, the experiments based on the person-post matching model are compared with a variety of person-post matching methods in the actual recruitment dataset, and the experimental results are analyzed.http://dx.doi.org/10.1155/2021/6206288 |
| spellingShingle | Xiaowei Wang Zhenhong Jiang Lingxi Peng A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs Complexity |
| title | A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs |
| title_full | A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs |
| title_fullStr | A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs |
| title_full_unstemmed | A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs |
| title_short | A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs |
| title_sort | deep learning inspired person job matching model based on sentence vectors and subject term graphs |
| url | http://dx.doi.org/10.1155/2021/6206288 |
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