Earticle

초록

영어

While youth unemployment has recorded the lowest level since the global COVID-19 pandemic, SMEs(small and medium sized enterprises) are still struggling to fill vacancies. It is difficult for SMEs to find good candidates as well as for job seekers to find appropriate job offers due to information mismatch. To overcome information mismatch, this study proposes the fine-turning model for bidirectional HR matching based on a pre-learning language model called BERT(Bidirectional Encoder Representations from Transformers). The proposed model is capable to recommend job openings suitable for the applicant, or applicants appropriate for the job through sufficient pre-learning of terms including technical jargons. The results of the experiment demonstrate the superior performance of our model in terms of precision, recall, and f1-score compared to the existing content-based metric learning model. This study provides insights for developing practical models for job recommendations and offers suggestions for future research.

목차

Abstract
1. 서론
2. 기존 연구
2.1 일자리 추천시스템
2.2 어텐션 메커니즘(Attention mechanism)과 BERT
3. BERT 기반의 전이학습 모델 설계
4. 실험
4.1 데이터 셋
4.2 성능평가 방법
4.3 실험 결과
5. 결론
References

키워드

저자

• 오소진 [ Sojin Oh | Doctoral Student, First Author, Department of Management Information Systems, Hannam university ] First Author
• 장문경 [ Moonkyoung Jang | Assistant Professor, Department of Global IT Business, Hannam university ] Co-author
• 송희석 [ Hee Seok Song | Professor, Department of Global IT Business, Hannam university ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005