ResumeGAN : an optimized deep representation learning framework for talent-job fit via adversarial learning

• Main Authors: Luo, Yong, Zhang, Huaizheng, Wen, Yonggang, Zhang, Xinwen
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering::Information systems::Information storage and retrieval; Resume Job-post Fit; Talent Recruitment
• Online Access: https://hdl.handle.net/10356/152987
• Institution: Nanyang Technological University

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.