Interactive Skill Based Labor Market Mechanics and Dynamics Analysis System Using Machine Learning and Big Data

  • Leo MrsicEmail author
  • Hrvoje Jerkovic
  • Mislav Balkovic
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)


Interest in talent recognition, talent recruitment, education and labor mobility has been on the rise in last years. Business sector is changing its human resources (HR) policies globally, changing ways in policies and practices related to employee management and employer branding. The process of talent recognition and/or search was mostly manual work, often carried by professional agencies or HR officer in organization. In today’s challenging labor market environment, this process is inefficient and slow with and often limited in success. In this paper we are focused on skills, education and lifelong learning domain which has an important role in the 10 priorities of the European Commission (EC) 2014–2019. Our starting point was to look for machine learning and big data techniques to support the policy makers and analysts in reducing mismatch between jobs and skills at regional level in the European Union (EU) through the use of data. Our research includes massive semi structured resume dataset (50+ million documents) combined with several official statistical surveys. We were able to leverage the advances in machine learning and big data to automate resume/skill classification and to improve productivity in skill-based labor market mechanics and dynamics analysis. This paper proposes a model of extracting important information after resumes are being partially classified, classify skills using European multilingual classification of Skills, Competences, Qualifications and Occupations (ESCO) by skill pillar matching and use modern interactive visualization tools to gain smart insights powered by geospatial and time-series analysis. Research goals are to point attention towards data science and machine learning and its usage in labor and educational market mechanics and dynamics.


Resume analytics Skill recognition Labor market dynamics and mechanics Interactive labor market visualization Talent management ESCO European Union labor market analytics Machine learning Big data Data science 


  1. 1.
    Smedt, J.D., Le Vrang, M., Papantoniou, A.: ESCO: Towards a Semantic Web for the European Labor Market (2015)Google Scholar
  2. 2.
    European Commission: ESCO, European classification of skills: The first public release. Publications Office of the European Union, Luxembourg (2013)Google Scholar
  3. 3.
  4. 4.
    Abbott, D.: Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst. Wiley, Indianapolis (2014)Google Scholar
  5. 5.
    Lin, N.: Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics. Pearson, Upper Saddle River (2015)Google Scholar
  6. 6.
    Putler, D.S., Krider, R.E.: Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R. CRC Press, Boca Raton (2012)Google Scholar
  7. 7.
    Otte, E., Rousseau, R.: Social network analysis: a powerful strategy, also for the information sciences. J. Inf. Sci. 28(6), 441–453 (2016)CrossRefGoogle Scholar
  8. 8.
    Wernicke, S., Rasche, F.: FANMOD: a tool for fast network motif detection. Bioinformatics 22(9), 1152–1153 (2006)CrossRefGoogle Scholar
  9. 9.
    European Commission: European Classification of Skills/Competences, Qualifications and Occupations. Booklet, Publications Office of the European Union, Luxembourg (2013)Google Scholar
  10. 10.
    Kofler, A., Prast, M.: OpenSKIMR A Job and Learning Platform. CS & IT-CSCP, pp. 95–106, January 2017Google Scholar
  11. 11.
    European Commission: ESCO Strategic framework - European Skills, Competences, Qualifications and Occupations, July 2017 (2017)Google Scholar
  12. 12.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Dijkstra’s algorithm. In: Introduction to Algorithms, 2nd edn, Chap. 24, pp. 595–599. MIT Press (2009)Google Scholar
  13. 13.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  14. 14.
    Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1–2), 245–271 (1997)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Shankar, S., Karypis, G.: A feature weight adjustment algorithm for document categorization. In: KDD-2000 Workshop on Text Mining, Boston, USA (2000)Google Scholar
  16. 16.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)zbMATHGoogle Scholar
  17. 17.
    Almuallim, H., Dietterich, T.G.: Efficient algorithms for identifying relevant features. Oregon (2014)Google Scholar
  18. 18.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Fu, S., Desmarais, M., Chen, W.: Reliability analysis of Markov blanket learning algorithms. IEEE (2010)Google Scholar
  20. 20.
    Sovren Group: Overview of the Sovren Semantic Matching Engine and Comparison to Traditional Keyword Search Engines. Sovren Group, Inc. (2006)Google Scholar
  21. 21.
    Rafter, R., Bradley, K., Smyth, B.: Automated collaborative filtering applications for online recruitment services. In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) AH 2000. LNCS, vol. 1892, pp. 363–368. Springer, Heidelberg (2000). Scholar
  22. 22.
    Malinowski, J., Keim, T., Wendt, O., Weitzel, T.: Matching people and jobs: a bilateral recommendation approach. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences, HICSS 2006, Hawaii, USA, 4–7 January 2006 (2006). 137cGoogle Scholar
  23. 23.
    Guo, X., Jerbi, H., O’Mahony, M.P.: An analysis framework for content-based job recommendation. Insight Centre for Data Analytics, Dublin (2014).
  24. 24.
    Al-Otaibi1, S.T., Ykhlef, M.: A survey of job recommender systems. Int. J. Phys. Sci. 7(29), 5127–5142 (2012)Google Scholar
  25. 25.
    Kotthoff, L., Gent, I.P., Miguel, I.: An evaluation of machine learning in algorithm selection for search problems. AI Commun. 25(3), 257–270 (2011)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Bojars, U., Breslin, J.G.: ResumeRDF: expressing skill information on the semantic web (2007)Google Scholar
  27. 27.
    Turney, P.D., Littman, M.: Unsupervised learning of semantic orientation from a hundred billion-word corpus. Technical report ERC-1094 (NRC 44929), National Research Council of Canada (2002)Google Scholar
  28. 28.
    Marjit, U., Sharma, K., Biswas, U.: Discovering resume information using linked data. Int. J. Web Semant. Technol. (IJWesT) 3(2), 51–61 (2012)CrossRefGoogle Scholar
  29. 29.
    Fazel-Zarandi1, M., Mark, S.: Fox2, semantic matchmaking for job recruitment: an ontology-based hybrid approach. In: IJCA Proceedings of the 3rd International SMR2 2009 Workshop on Service Matchmaking and Resource Retrieval in the Semantic Web (2013)Google Scholar
  30. 30.
    Kopparapu, S.K.: Automatic extraction of usable information from unstructured resumes to aid search. In: IEEE International Conference on Progress in Informatics and Computing (PIC) (2010)Google Scholar
  31. 31.
    Jiang, Z.X., Zhang, C., Xiao, B., Lin, Z.: Research and implementation of intelligent Chinese resume parsing. In: WRI International Conference on Communications and Mobile Computing (2009)Google Scholar
  32. 32.
    Chuang, Z., Ming, W., Guang, L.C., Bo, X.: Resume parser: semi-structured Chinese document analysis. In: WRI World Congress on Computer Science and Information Engineering (2009)Google Scholar
  33. 33.
    Celik, D., Karakas, A., Bal, G., Gultunca, C.: Towards an information extraction system based on ontology to match resumes and jobs. In: IEEE 37th Annual Workshops on Computer Software and Applications Conference Workshops (2013)Google Scholar
  34. 34.
    Wu, D., Wu, L., Sun, T., Jiang, Y.: Ontology based information extraction technology. In: International Conference on Internet Technology and Applications (iTAP) (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Algebra University CollegeZagrebCroatia

Personalised recommendations