Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152987
Title: ResumeGAN : an optimized deep representation learning framework for talent-job fit via adversarial learning
Authors: Luo, Yong
Zhang, Huaizheng
Wen, Yonggang
Zhang, Xinwen
Keywords: Engineering::Computer science and engineering::Information systems::Information storage and retrieval
Issue Date: 2019
Source: Luo, Y., Zhang, H., Wen, Y. & Zhang, X. (2019). ResumeGAN : an optimized deep representation learning framework for talent-job fit via adversarial learning. The 28th ACM International Conference on Information and Knowledge Management, 1101-1110. https://dx.doi.org/10.1145/3357384.3357899
Project: NRF2015ENCGDCR01001- 003
NRF2015ENCGBICRD001- 012
Abstract: Nowadays, it is popular to utilize online recruitment services for talent recruitment and job recommendation. Given the vast amounts of online talent profiles and job-posts, it is labor-intensive and exhausted for recruiters to manually select only a few potential candidates for further consideration, and also nontrivial for talents to find the most matched job positions. Recently, some deep learning-based approaches are developed to automatically matching the talent resumes and job requirements, and have achieved encouraging performance. In this paper, we propose a novel framework that targets the same task, but integrate different types of information in a more sophisticated way and introduce adversarial learning to learn more expressive representation. In addition, we build a dataset for model evaluation and the effectiveness of our framework is demonstrated by extensive experiments.
URI: https://hdl.handle.net/10356/152987
ISBN: 9781450369763
DOI: 10.1145/3357384.3357899
Rights: © 2019 Association for Computing Machinery. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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