Journal of Information Technology Applications and Management

Korea Data Strategy Society (한국데이터전략학회)
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A BERT-based Transfer Learning Model for Bidirectional HR Matching

양방향 인재매칭을 위한 BERT 기반의 전이학습 모델

  • Oh, Sojin (Department of Management Information Systems, Hannam university) ;
  • Jang, Moonkyoung (Department of Global IT Business, Hannam university) ;
  • Song, Hee Seok (Department of Global IT Business, Hannam university)
  • Received : 2021.05.31
  • Accepted : 2021.07.11
  • Published : 2021.08.30
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Abstract

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.

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References

  1. Bahdanau, D., Cho, K., and Bengio, Y., "Neural machine translation by jointly learning to align and translate", CoRR, abs/1409.0473, 2014.
  2. Cho, K., van Merrienboer, B., Gulcehre, C., Bougares, F., Schwenk, H., and Bengio, Y., "Learning phrase representations using rnn encoder-decoder for statistical machine translation", CoRR, abs/1406.1078, 2014.
  3. Han, J., Kim, J., Jeon, W., Kim, H., and Hong, Y., "A Counseling Matching System Using BERT Language Model", The Korean Institute of Information Scientists and Engineers, pp. 1566-1568, 2020.
  4. Hochreiter, S., Bengio, Y., Frasconi, P., and Schmidhuber, J., "Gradient flow in recurrent nets: The difficulty of learning long-term dependencies", 2001.
  5. Hong, S., Na, S., Kim, K. W., Shin, B., and Chung, Y., "BERT for Alzheimer's Disease and Schizophrenia diagnosis", The Korean Institute of Information Scientists and Engineers, pp. 419-421. 2020.
  6. Jacob, D., Chang, M., Lee, K., and Kristina, T., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", arXiv preprint at arXiv:1810.04805v1, 2018.
  7. Kim, J., "Analysis on the Increase in Unemployment Rates Since 2014", KDI (Korea Development Institute) Feature Article (2018.11. 06) Eng, 2018.
  8. Kim, Y., Denton, C., Hoang, L., and Rush, A. M., "Structured attention networks", In International Conference on Learning Representations, 2017.
  9. Le, Q. V. and Mikolov, T., "Distributed Representations of Sentences and Documents", Proceeding of International Conference on Machine Learning(ICML), 2014.
  10. Luong, M., Hieu, P., and Christopher, D. M., "Effective approaches to attentionbased neural machine translation", arXiv preprint arXiv:1508.04025, 2015.
  11. Maheshwary, S. and Misra, H., "Matching Resumes to Jobs via Deep Siamese Network", Proceedings of the The Web Conference 2018 (WWW '18), 2018, pp. 87-88.
  12. Pessemier, T. D., Vanhecke, K., and Martens, L., "A scalable, high-performance Algorithm for hybrid job recommendations", Proceedings of the Recommender Systems Challenge (RecSys Challenge '16), 2016.
  13. SKTBrain, "Korean BERT pre-trained cased (KoBERT)", https://github.com/SKTBrain/KoBERT.
  14. Son, B., "NCS Utilization in Recruitment and Selection : The case of HRDKorea.", Korean Management Consulting Review, Vol. 15, No. 4, 2015, pp. 217-228.
  15. Song, H. S., "A design of content-based metric learning model for HR matching", Journal of Information Technology Applications & Management, Vol. 27, No. 6, 2020, pp. 141-151. https://doi.org/10.21219/JITAM.2020.27.6.141
  16. Statistics Korea, "December 2020 and Annual Employment Trends", 2020,12.
  17. Sutskever, I., Vinyals, O., and Le, Q. V., "Sequence to sequence learning with neural networks", In Advances in Neural Information Processing Systems, 2014, pp. 3104-3112.
  18. Valverde-Rebaza, J., Puma, R., Bustios, P., and Silva, N. C., "Job Recommendation based on Job Seeker Skills: An Empirical Study", Proceedings of the First Workshop on Narrative Extraction From Text (Text2Story 2018), 2018, pp. 47-51.
  19. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., and Polosukhin, I., "Attention is all you need", arXiv preprint arXiv:1706.03762. 2017.
  20. Yoo, S. and Jeong, O., "An Intelligent Chatbot Utilizing BERT Model and Knowledge Graph", The Journal of Society for e-Business Studies, Vol. 24, No. 3, 2019, pp. 87-98.