Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Job Preference Analysis and Job Matching System Development for the Middle Aged Class

중장년층 일자리 요구사항 분석 및 인력 고용 매칭 시스템 개발

  • Kim, Seongchan (Graduate School of Knowledge Service Engineering, KAIST) ;
  • Jang, Jincheul (Graduate School of Knowledge Service Engineering, KAIST) ;
  • Kim, Seong Jung (Graduate School of Knowledge Service Engineering, KAIST) ;
  • Chin, Hyojin (Graduate School of Knowledge Service Engineering, KAIST) ;
  • Yi, Mun Yong (Graduate School of Knowledge Service Engineering, KAIST)
  • 김성찬 (한국과학기술원 지식서비스공학대학원) ;
  • 장진철 (한국과학기술원 지식서비스공학대학원) ;
  • 김성중 (한국과학기술원 지식서비스공학대학원) ;
  • 진효진 (한국과학기술원 지식서비스공학대학원) ;
  • 이문용 (한국과학기술원 지식서비스공학대학원)
  • Received : 2016.08.04
  • Accepted : 2016.10.06
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

With the rapid acceleration of low-birth rate and population aging, the employment of the neglected groups of people including the middle aged class is a crucial issue in South Korea. In particular, in the 2010s, the number of the middle aged who want to find a new job after retirement age is significantly increasing with the arrival of the retirement time of the baby boom generation (born 1955-1963). Despite the importance of matching jobs to this emerging middle aged class, private job portals as well as the Korean government do not provide any online job service tailored for them. A gigantic amount of job information is available online; however, the current recruiting systems do not meet the demand of the middle aged class as their primary targets are young workers. We are in dire need of a specially designed recruiting system for the middle aged. Meanwhile, when users are searching the desired occupations on the Worknet website, provided by the Korean Ministry of Employment and Labor, users are experiencing discomfort to search for similar jobs because Worknet is providing filtered search results on the basis of exact matches of a preferred job code. Besides, according to our Worknet data analysis, only about 24% of job seekers had landed on a job position consistent with their initial preferred job code while the rest had landed on a position different from their initial preference. To improve the situation, particularly for the middle aged class, we investigate a soft job matching technique by performing the following: 1) we review a user behavior logs of Worknet, which is a public job recruiting system set up by the Korean government and point out key system design implications for the middle aged. Specifically, we analyze the job postings that include preferential tags for the middle aged in order to disclose what types of jobs are in favor of the middle aged; 2) we develope a new occupation classification scheme for the middle aged, Korea Occupation Classification for the Middle-aged (KOCM), based on the similarity between jobs by reorganizing and modifying a general occupation classification scheme. When viewed from the perspective of job placement, an occupation classification scheme is a way to connect the enterprises and job seekers and a basic mechanism for job placement. The key features of KOCM include establishing the Simple Labor category, which is the most requested category by enterprises; and 3) we design MOMA (Middle-aged Occupation Matching Algorithm), which is a hybrid job matching algorithm comprising constraint-based reasoning and case-based reasoning. MOMA incorporates KOCM to expand query to search similar jobs in the database. MOMA utilizes cosine similarity between user requirement and job posting to rank a set of postings in terms of preferred job code, salary, distance, and job type. The developed system using MOMA demonstrates about 20 times of improvement over the hard matching performance. In implementing the algorithm for a web-based application of recruiting system for the middle aged, we also considered the usability issue of making the system easier to use, which is especially important for this particular class of users. That is, we wanted to improve the usability of the system during the job search process for the middle aged users by asking to enter only a few simple and core pieces of information such as preferred job (job code), salary, and (allowable) distance to the working place, enabling the middle aged to find a job suitable to their needs efficiently. The Web site implemented with MOMA should be able to contribute to improving job search of the middle aged class. We also expect the overall approach to be applicable to other groups of people for the improvement of job matching results.

저출산 및 인구 고령화가 가속화되면서, 중장년 퇴직자 등 노동 소외 계층의 취업난 해결은 우리 사회의 핵심 과제로 등장하고 있다. 온라인에는 수많은 일자리 요구 정보가 산재해 있으나, 이를 중장년 구직자에게 제대로 매칭시키지는 못하고 있다. 워크넷 취업 로그에 따르면 구직자가 선호하는 직종에 취업하는 경우는 약 24%에 불과하다. 그러므로, 이러한 문제를 극복하기 위해서는 구직자에게 일자리 정보를 매칭시킬 때 선호하는 직종과 유사한 직종들을 추천하는 소프트 매칭 기법이 필수적이다. 본 연구는 중장년층에 특화된 소프트 직업 매칭 알고리즘과 서비스를 고안하고 개발하여 제공하는 것을 목표로 한다. 이를 위하여 본 연구에서는 1) 대용량의 구직 활동 기록인 워크넷 로그로부터 중장년층의 일자리 특성 및 요구 추세를 분석하였다. 2) 중장년층의 일자리 추천을 위해 직종 유사도 기준으로 일자리 분류표(KOCM)를 재정렬하였다. 이 결과를 이용하여, 3) 중장년에 특화된 인력 고용 소프트 매칭 직업 추천 알고리즘(MOMA)을 개발하여 구인 구직 웹사이트에 적용하였다. 자체 저작한 중장년층 특화 일자리 분류표(KOCM)를 이용한 소프트 일자리 매칭 시스템의 정확도를 측정하였을 때, 실제 고용 결과 기준, 하드 매칭 대비 약 20여 배의 성능 향상을 보였다. 본 연구내용을 적용하여 개발한 중장년층 특화 구직 사이트는 중장년층의 구직 과정에서 입력 정보 부담을 최소화하고 소프트 매칭을 통해 사용자의 요구직종에 적합한 일자리를 정확하고 폭넓게 추천함으로 중장년층의 삶의 질 향상에 기여할 수 있을 것으로 기대된다.

Keywords

References

  1. Aamodt, A., and E. Plaza, "Case-based reasoning: Foundational issues, methodological variations, and system approaches," AI Communications, Vol.7, No.1(1994), 39-59.
  2. Adomavicius, G., and A. Tuzhilin, "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions," IEEE Transactions on Knowledge and Data Engineering, Vol.17,No 6(2005), 734-749. https://doi.org/10.1109/TKDE.2005.99
  3. Cho, I., "A Study on Soft Matching and Job Similarity for Matching Effectiveness," Employment Issue (2010).
  4. Gower, J. C., "A General Coefficient of Similarity and Some of Its Properties," Biometrics, Vol.27, No.4(1971), 857-871. https://doi.org/10.2307/2528823
  5. Hong, W., L. Li, T. Li, and W. Pan, "iHR: an online recruiting system for Xiamen Talent Service Center," Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (2013). 1177-1185.
  6. Jang, J., S. Jung, S. Lee, C. Jung, W. C. Yoon, and M. Y. Yi, "Learning Material Bookmarking Service based on Collective Intelligence," Journal of Intelligence and Information Systems, Vol.20, No.2 (2014). 179-192. https://doi.org/10.13088/jiis.2014.20.2.179
  7. Jang, J., D. Zhao, S. Kim, H. Chin, S. J. Kim, and M. Y. Yi, "Requirement Analysis of Personalized Job Matching Service for Middle-Aged Adults in South Korea," Proceedings of the HCI Society of Korea, (2016), 68-69.
  8. Jannach, D., M. Zanker, A. Felfernig, and G. Friedrich, Recommender Systems: An Introduction. Cambridge University Press. 2010.
  9. KAIST, Job Classification Table for Middle-Aged Adults' Personalized Job Matching, Copyright (C-2016-009103), 2016.
  10. KEIS, Job Placement Classification Manual, 2009.
  11. KEIS, Korea Dictionary of Occupations, 2016.
  12. Kim, K., Y. Ham, and K. Lee, "Analysis on Factors Influencing Welfare Spending of Local Authority: Implementing the Detailed Data Extracted from the Social Security Information System," Journal of Intelligence and Information Systems, Vol.9, No.2(2013). 141-156.
  13. Kim, M., and Y. Cho, "A Multimodal Profile Ensemble Approach to Development of Recommender Systems Using Big Data," Journal of Intelligence and Information Systems, Vol.21, No.4(2015). 93-110. https://doi.org/10.13088/JIIS.2015.21.4.093
  14. Kim, S. J., H. Jung, J. Gwon, S. Oh, and W. Kim, "An Analysis on Employment Support Services for Vulnerable Social Group," Hannam University Institute of Social Sciences, (2005).
  15. Kim, S. J., S. Kim, J. Jang, H. Chin and M. Y. Yi, "Job Preference and Selection Analysis of Senior People for Developing Senior Job Recommendation Algorithm," Proceedings of the Korea Computer Congress, (2016).
  16. Lee, D. H., and P. Brusilovsky, "Fighting information overflow with personalized comprehensive information access: A proactive job recommender," The 3rd International Conference on Autonomic and Autonomous Systems, ICAS'07, (2007).
  17. Park, S., J. Cho, H. Jun, J. Kim and J. Ryu, "Development of Job Matching Function," Quarterly Journal of Labor Policy, Vol.12, No.3(2012), 25-51.
  18. Rafter, R., K. Bradley, and B. Smyth, "Personalised Retrieval for Online Recruitment Services," Proceedings of the 22th BCSIRSG Colloquium on IR Research, (2000), 151-163.
  19. Song, H., "Clustering method based on similarity calculation of Mixed Data," Master Thesis, Department of Computer Engineering, Jeju National University, 2015.