Application of Machine Learning Algorithms to an online Recruitment System

Evanthia Faliagka · Kostas Ramantas · Athanasios Tsakalidis · Giannis E. Tzimas

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Abstract

Abstract — In this work, we present a novel approach for evaluating job applicants in online recruitment systems, leveraging machine learning algorithms to solve the candidate ranking problem. 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 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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1999Linguistic styles: Language use as an individual difference.

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2023Navigating the Paradigm Shift in HRM Practices Through the Lens of Artificial Intelligence: A Post-pandemic Perspective 2020AN AUTOMATED RESUME SCREENING SYSTEM USING NATURAL LANGUAGE PROCESSING AND SIMILARITY 2013Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT)2021Design and development of machine learning based resume ranking system 2021literature Review: Artificial Intelligence Impact on the Recruitment ProcessA LITERATURE REVIEW: ARTIFICIAL INTELLIGENCE IMPACT ON THE RECRUITMENT PROCESS 2016A novel approach to evaluate and rank candidates in a recruitment process by estimating emotional intelligence through social media data 2015An Automatic Online Recruitment System Based on Exploiting Multiple Semantic Resources and Concept-Relatedness Measures 2022Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification

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