Improving Employee Recruitment Through Data Mining

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)


Companies have always struggled with recruiting suitable candidates. In this age of data, we believe that the process of recruiting candidates is broken. This paper presents our efforts to improve the process by introducing data analytics and smart decision making. Recruiters and recruiting companies can benefit from such findings by analyzing key performance indicators and recommendation systems when recruiting new candidates. Furthermore, we propose an approach of identifying employment trends as well as new skills that are required by the job market. The procedure is fully automatic and relies on machine learning approaches.


Recruitment Clustering Data analytics Prediction systems 


  1. 1.
    Chien, C.-F., Chen, L.-F.: Data mining to improve personnel selection and enhance human capital: a case study in high-technology industry. Expert Syst. Appl. 34(1), 280–290 (2008). ElsevierMathSciNetCrossRefGoogle Scholar
  2. 2.
    Giri, A., Ravikumar, A., Mote, S., Bharadwaj, R.: Vritthi - a theoretical framework for IT recruitment based on machine learning techniques applied over Twitter, LinkedIn, SPOJ and GitHub profiles. In: 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), Ernakulam, pp. 1–7 (2016)Google Scholar
  3. 3.
    Javed, F., Luo, Q.: Carotene: a job classification system for the online recruitment domain. In: 2015 IEEE First International Conference on Big Data Computing Service and Applications (2015)Google Scholar
  4. 4.
    Singh, S., Kumar, V.: Performance analysis of engineering students for recruitment using classification data mining techniques. Int. J. Sci. Eng. Comput. Technol. 3(2), 31 (2013)Google Scholar
  5. 5.
    Jantan, H., Hamdan, A.R., Othman, Z.A.: Towards applying data mining techniques for talent mangement. In: International Conference on Computer Engineering and Applications, vol. 2 (2011)Google Scholar
  6. 6.
    Azar, A., Sebt, M.V., Ahmadi, P., Rajaeian, A.: A model for personnel selection with a data mining approach: a case study in a commercial bank. SA J. Hum. Resour. Manag. 11(1), 10 (2013). Tydskrif vir Menslikehulpbronbestuur, Art. #449CrossRefGoogle Scholar
  7. 7.
    Marseco Software: vYou platform. Accessed Nov 2017
  8. 8.
    Marseco Software: cvmanager platform. Accessed Nov 2017
  9. 9.
    Laumer, S., Eckhardt, A.: Help to find the needle in a haystack: integrating recommender systems in an it supported staff recruitment system. In: Proceedings of the Special Interest Group on Management Information System’s 47th Annual Conference on Computer Personnel Research, pp. 7–12 (2009)Google Scholar
  10. 10.
    Diaby, M., et al.: Toward the next generation of recruitment tools: an online social network-based job recommender system. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 821–828 (2013)Google Scholar
  11. 11.
    Quintini, G.: Over-qualified or under-skilled: a review of existing literature. OECD Social, Employment, and Migration Working Papers, No. 121, OECD Publishing, Paris (2011)Google Scholar
  12. 12.
    Green, F., McIntosh, S.: Is there a genuine under-utilization of skills amongst the over-qualified? Appl. Econ. 39(4), 427–439 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.South East European UniversityTetovoMacedonia

Personalised recommendations