Skip to main content

Improving Employee Recruitment Through Data Mining

  • Conference paper
Trends and Advances in Information Systems and Technologies (WorldCIST'18 2018)

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

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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). Elsevier

    Article  MathSciNet  Google Scholar 

  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. 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. 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. 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. 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. #449

    Article  Google Scholar 

  7. Marseco Software: vYou platform. https://www.vyou.ch. Accessed Nov 2017

  8. Marseco Software: cvmanager platform. https://www.cvmanager.ch. Accessed Nov 2017

  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. 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. 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. Green, F., McIntosh, S.: Is there a genuine under-utilization of skills amongst the over-qualified? Appl. Econ. 39(4), 427–439 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Visar Shehu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Cite this paper

Shehu, V., Besimi, A. (2018). Improving Employee Recruitment Through Data Mining. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-77703-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77703-0_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77702-3

  • Online ISBN: 978-3-319-77703-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics