AI-Based Recruiting: The Future Ahead

Martinez-Gil, Jorge AI-Based Recruiting: The Future Ahead., 2021 [Preprint]

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English abstract

The Human Resources industry is currently being revolutionized by the automation of tedious and time-consuming aspects of their processes. Since AI paradigms such as deep neural networks and other machine learning methods can make accurate predictions and analyze vast amounts of information, these technologies are suitable for facing some of the major challenges in this domain. We overview here how this industry is changing; from the automatic screening of the candidates to bias removal in most of the processes, through techniques for the automatic discovery of potential employees or new advances for improving the candidate's experience.

Item type: Preprint
Keywords: Big Data Technologies, Text Mining, Information Systems
Subjects: L. Information technology and library technology > LP. Intelligent agents.
L. Information technology and library technology > LZ. None of these, but in this section.
Depositing user: Dr Jorge Martinez-Gil
Date deposited: 28 Jan 2021 14:44
Last modified: 28 Jan 2021 14:44
URI: http://hdl.handle.net/10760/40987

References

Frank Färber, Tim Weitzel, Tobias Keim:An Automated Recommendation Approach to Selection in Personnel Recruitment. AMCIS 2003: 302.

Kunmei Wen, Yong Zeng, Ruixuan Li, Jian Qiang Lin: Modeling semantic information in engineering applications: a review. Artif. Intell. Rev. 37(2): 97-117 (2012).

Jorge Martinez-Gil, Jose F. Aldana-Montes: An overview of current ontology meta-matching solutions. Knowledge Eng. Review 27(4): 393-412 (2012).

Jorge Martinez-Gil, Alejandra Lorena Paoletti, Klaus-Dieter Schewe: A Smart Approach for Matching, Learning and Querying Information from the Human Resources Domain. ADBIS (Short Papers and Workshops) 2016: 157-167.

V. Senthil Kumaran, A. Sankar: Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT). IJMSO 8(1): 56-64 (2013).

Jorge Martinez-Gil: An overview of textual semantic similarity measures based on web intelligence. Artif. Intell. Rev. 42(4): 935-943 (2014).


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