On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV

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.

[1]  Lyndon J. B. Nixon,et al.  Improving the Accuracy of Job Search with Semantic Techniques , 2007, BIS.

[2]  Jürgen Dorn,et al.  ONTOLOGY DEVELOPMENT FOR HUMAN RESOURCE MANAGEMENT , 2007 .

[3]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[4]  K. Ramar,et al.  Applicability of Clustering and Classification Algorithms for Recruitment Data Mining , 2010 .

[5]  G. Mishne.  Experiments with Mood Classification in , 2005 .

[6]  James W. Pennebaker,et al.  Linguistic Inquiry and Word Count (LIWC2007) , 2007 .

[7]  Dirk Heylen,et al.  Dominance Detection in Meetings Using Easily Obtainable Features , 2005, MLMI.

[8]  Giannis Tzimas,et al.  An Integrated E-Recruitment System for Automated Personality Mining and Applicant Ranking , 2012, Internet Res..

[9]  J. Pennebaker,et al.  Linguistic styles: language use as an individual difference. , 1999, Journal of personality and social psychology.

[10]  Sharon Pande.  E‐recruitment creates order out of chaos at SAT Telecom , 2011 .

[11]  Sofia Stamou,et al.  A Personality Mining System for Automated Applicant Ranking in Online Recruitment Systems , 2011, ICWE.

[12]  Elena Simperl,et al.  Practical Guidelines for Building Semantic eRecruitment Applications , 2006 .

[13]  Wahiba Ben Abdessalem Karaa,et al.  Web-based recruiting , 2010, ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010.

[14]  Juan-Manuel Torres-Moreno,et al.  E-Gen: Automatic Job Offer Processing System for Human Resources , 2007, MICAI.

[15]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[16]  Francky Trichet,et al.  Ontology-Based Systems Dedicated to Human Resources Management: An Application in e-Recruitment , 2006, OTM Workshops.

[17]  Jon Oberlander,et al.  What Are They Blogging About? Personality, Topic and Motivation in Blogs , 2009, ICWSM.

[18]  Giannis Tzimas,et al.  Application of Machine Learning Algorithms to an online Recruitment System , 2012, ICIW 2012.

[19]  Jon Oberlander,et al.  Whose Thumb Is It Anyway? Classifying Author Personality from Weblog Text , 2006, ACL.

[20]  Giannis Tzimas,et al.  An Integrated e-Recruitment System for CV Ranking based on AHP , 2011, WEBIST.

[21]  Marilyn A. Walker,et al.  Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text , 2007, J. Artif. Intell. Res..

[22]  Robert Tolksdorf,et al.  The Impact of Semantic Web Technologies on Job Recruitment Processes , 2005, Wirtschaftsinformatik.

[23]  Giovanni Quattrone,et al.  An XML-Based Multiagent System for Supporting Online Recruitment Services , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  Mathieu Roche,et al.  Job Offer Management: How Improve the Ranking of Candidates , 2009, ISMIS.

[25]  Helmut Schmid,et al.  Improvements in Part-of-Speech Tagging with an Application to German , 1999 .