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Taxonomy Development and Its Impact on a Self-learning e-Recruitment System

Abstract : In this work we present a novel approach for evaluating job applicants in online recruitment systems, using machine learning algorithms to solve the candidate ranking problem and performing semantic matching techniques. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants’ LinkedIn profile, and compares them semantically to the job’s prerequisites. It also infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters, thus it can be trusted for the automation of applicant ranking and personality mining.
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Submitted on : Thursday, May 11, 2017 - 5:10:26 PM
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Evanthia Faliagka, Ioannis Karydis, Maria Rigou, Spyros Sioutas, Athanasios Tsakalidis, et al.. Taxonomy Development and Its Impact on a Self-learning e-Recruitment System. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.164-174, ⟨10.1007/978-3-642-33409-2_18⟩. ⟨hal-01521405⟩



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