원문정보
A Design of Content-based Metric Learning Model for HR Matching
초록
영어
The job mismatch between job seekers and SMEs is becoming more and more intensifying with the serious difficulties in youth employment. In this study, a bi-directional content-based metric learning model is proposed to recommend suitable jobs for job seekers and suitable job seekers for SMEs, respectively. The proposed model not only enables bi-directional recommendation, but also enables HR matching without relearning for new job seekers and new job offers. As a result of the experiment, the proposed model showed superior performance in terms of precision, recall, and f1 than the existing collaborative filtering model named NCF+GMF. The proposed model is also confirmed that it is an evolutionary model that improves performance as training data increases.
목차
1. 서론
2. 기존 연구
3. 인재매칭 모형의 설계
4. 실험
4.1 데이터 셋
4.2 성능평가 방법
4.3 실험 결과
5. 결론
References