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A Case Study on the Personalized Online Recruitment Services : Focusing on Worldjob+'s Use of Splunk

개인화된 구직정보서비스 제공에 관한 사례연구 : 월드잡플러스의 스플렁크 활용을 중심으로

  • Received : 2017.12.13
  • Accepted : 2018.02.20
  • Published : 2018.02.28

Abstract

Online recruitment services have emerged as one of the most popular Internet services, providing job seekers with a comprehensive list of jobs and a search engine. But many recruitment services suffer from shortcomings due to their reliance on traditional client-pull information access model, in manay cases resulting in unfocused search results. Worldjob+, being operated by The Human Resources Development Service of Korea, addresses these problems and uses Splunk, a platform for analyzing machine data, to provide a more proactive and personalised services. It focuses on enhancing the existing system in two different ways: (a) using personalised automated matching techniques to proactively recommend most preferrable profile or specification information for each job opening announcement or recruiting company, (b) and to recommend most preferrable or desirable job opening announcement for each job-seeker. This approach is a feature-free recommendation technique that recommends information items to a given user based on what similar users have previously liked. A brief discussion about the potential benefit is also provided as a conclusion.

Keywords

Online recruitment service;Worldjob+;Splunk;machine data;matching technique

References

  1. S. H. Kim, S. J. Kang & J. E. Choi. (2014). Policy for Poviding Young People with Jobs : New Direction for Answers. Policy Report. Korea New Institute for Society, 2014.
  2. S. J. Park. (2015). Tasks for Strengthening the Global Competitivensess of Overseas Employment and Intern Projects for Young People. The HRD Review, 8(4), 48-64.
  3. V. Brencic. (2014). Search online: Evidence from acquisition of information on online job boards and resume banks. Journal of Economic Psychology, 42, 112-125. https://doi.org/10.1016/j.joep.2014.02.003
  4. M. Lee. (2011). Big Data and the Utilization of Public Data. Internet and Information Security, 2(2), 47-64.
  5. Y. Hahm. (2017), "Data Integration Strategy in Big Data Era: A Public Sector Case Analysis. Journal of Information Technology and Architecture, 14(2), 115-128.
  6. S. H. Kim, H. S. Shin & S. H. Son. (2014). A Study on Large-Scale Traffic Information Modeling using R. Journal of KIISE : Computer Systems and Theory, 41(4), 151-157.
  7. B. Y. Lee, J. T. Lim & J. S. Yoo. (2013). Utilization of Social Media Analysis using Big Data. Journal of the Korea Contents Association, 13(2), 211-219. https://doi.org/10.5392/JKCA.2013.13.02.211
  8. https://www.worldjob.or.kr/intro.do
  9. S. Kim. (2016). A Study on the Characteristics of Job-Seeking Patterns of Younger Generation. A Journal of Job and Employment Service, 11(2), 35-54.
  10. R. Minas. (2014). One‐stop shops: Increasing employability and overcoming welfare state fragmentation?. International Journal of Social Welfare, 23(S1).
  11. C. Lindsay, R. W. McQuaid, & M. Dutton. (2008). Interagency cooperation and new approaches to employability. Social Policy & Administration, 42(7), 715-732. https://doi.org/10.1111/j.1467-9515.2008.00634.x
  12. C. R. Wanberg, Z. Zhang, & E. W. Diehn. (2010). Development of the "Getting Ready for Your Next Job" inventory for unemployed individuals. Personnel Psychology, 63(2), 439-478. https://doi.org/10.1111/j.1744-6570.2010.01177.x
  13. P. Russo,. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.
  14. J. Malinowski, T. Weitzel, & T. Keim. (2008). Decision support for team staffing: An automated relational recommendation approach. Decision Support Systems, 45(3), 429-447. https://doi.org/10.1016/j.dss.2007.05.005
  15. Sovren Group. (2006). Overviewof the Sovren Semantic Matching Engine And Comparison to Traditional Keyword Search Engines. Sovren Group, Inc.
  16. Y. Lu, S. El Helou, & D. Gillet. (2013, May). A recommender system for job seeking and recruiting website. In Proceedings of the 22nd International Conference on World Wide Web (pp. 963-966). ACM.
  17. R. Rafter, K. Bradley, & B. Smyth. (2000, August). Automated collaborative filtering applications for online recruitment services. In International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 363-368). Springer, Berlin, Heidelberg.