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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| Published in | Complexity (New York, N.Y.) Vol. 2021; no. 1 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Hindawi
2021
John Wiley & Sons, Inc Wiley |
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| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Wang, Xiaowei Jiang, Zhenhong Peng, Lingxi |
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| ContentType | Journal Article |
| Copyright | Copyright © 2021 Xiaowei Wang et al. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Xiaowei Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| Title | A Deep-Learning-Inspired Person-Job Matching Model Based on Sentence Vectors and Subject-Term Graphs |
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