International Conference on Human Interface and the Management of Information

HIMI 2015: Human Interface and the Management of Information. Information and Knowledge Design pp 115-126 | Cite as

A Team Hiring Solution Based on Graph-Based Modelling of Human Resource Entities

  • Avinash Sharma
  • Jyotirmaya Mahapatra
  • Asmita Metrewar
  • Abhishek Tripathi
  • Partha Dutta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9172)

Abstract

As modern organizations become more agile and support more complex business processes, acquiring the right set of talent is becoming crucial for their operations. One of the key talent acquisition problems is staffing a team that has requirement for multiple job descriptions, from a pool of external candidates. This team hiring problem may arise for (i) a new organization, (ii) a new group in an existing organization, or (iii) an existing group that faces high attrition level. This paper presents a Talent Acquisition Decision Support System (TADSS) that provides decision support for team hiring. The system first builds a weighted graph based model for the three types of Human Resource (HR) entities in the problem setup (jobs, employees and candidates), and the inter-relationship among them. Next, an algorithm based on spectral embedding of the HR Graph is used to select teams. The system then provides an interactive team selection and comparison interface based on the HR Graph. Simulation-based evaluations show the effectiveness of the proposed system in team formation.

Keywords

Human resource graph Graph embedding 

References

  1. 1.
    Aksin, Z., Armony, M., Mehrotra, V.: The modern call center: a multi-disciplinary perspective on operations management research. In: Production and Operations Management (2007)Google Scholar
  2. 2.
    Chung, F.R.K.: Spectral Graph Theory. American Mathematical Society, Providence (1997)MATHGoogle Scholar
  3. 3.
    Qiu, H., Hancock, E.R.: Clustering and embedding using commute times. Pattern Anal. Mach. Intell. 29(11), 1873–1890 (2007)CrossRefGoogle Scholar
  4. 4.
    Lehoucq, R.B., Sorensen, D.C., Yang, C.: ARPACK Users Guide: Solution of Large Scale Eigenvalue Problems by Implicitly Restarted Arnoldi Methods. Society for Industrial and Applied Mathematics, Philadelphia (1997)MATHGoogle Scholar
  5. 5.
    Mehta, S., Pimplikar, R., Singh, A., Varshney, L., Visweswariah, K.: Efficient multifaceted screening of job applicants. In: EDBT (2013)Google Scholar
  6. 6.
    Hoye, G., Hooft, E.A.J., Lievens, F.: Networking as a job search behavior: a social network perspective. J. Occup. Organ. Psychol 82(3), 661–682 (2009)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Social networking job matching technology. United States Patent Application 20130013526Google Scholar
  9. 9.
    Method and apparatus for hiring using social networks. United States Patent Application 20110196802Google Scholar
  10. 10.
    Krauth, B.V.: A dynamic model of job networking and social influences on employment. J. Economic Dyn. & Control 28(6), 1185–1204 (2004)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Determination of a contractor team. WIPO Patent Application WO/2013/187866Google Scholar
  12. 12.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Avinash Sharma
    • 1
  • Jyotirmaya Mahapatra
    • 1
  • Asmita Metrewar
    • 1
  • Abhishek Tripathi
    • 1
  • Partha Dutta
    • 1
  1. 1.Xerox Research Centre IndiaBangaloreIndia

Personalised recommendations