A hybrid approach to managing job offers and candidates

The evolution of the job market has resulted in traditional methods of recruitment becoming insufficient. As it is now necessary to handle volumes of information (mostly in the form of free text) that are impossible to process manually, an analysis and assisted categorization are essential to address this issue. In this paper, we present a combination of the E-Gen and Cortex systems. E-Gen aims to perform analysis and categorization of job offers together with the responses given by the candidates. E-Gen system strategy is based on vectorial and probabilistic models to solve the problem of profiling applications according to a specific job offer. Cortex is a statistical automatic summarization system. In this work, E-Gen uses Cortex as a powerful filter to eliminate irrelevant information contained in candidate answers. Our main objective is to develop a system to assist a recruitment consultant and the results obtained by the proposed combination surpass those of E-Gen in standalone mode on this task.

[1]  Juan-Manuel Torres-Moreno,et al.  Condensés de textes par des méthodes numériques , 2012, ArXiv.

[2]  Alex M. Andrew,et al.  ROBOT LEARNING, edited by Jonathan H. Connell and Sridhar Mahadevan, Kluwer, Boston, 1993/1997, xii+240 pp., ISBN 0-7923-9365-1 (Hardback, 218.00 Guilders, $120.00, £89.95). , 1999, Robotica (Cambridge. Print).

[3]  Robert Tolksdorf,et al.  Semantic-Web-Technologien im Arbeitsvermittlungsprozess , 2006, Wirtsch..

[4]  Stephen E. Robertson,et al.  Okapi at TREC-3 , 1994, TREC.

[5]  Jürgen Dorn,et al.  Meta-Search in Human Resource Management , 2007 .

[6]  Juan-Manuel Torres-Moreno,et al.  E-Gen : Profilage automatique de candidatures , 2008 .

[7]  Justin Zobel,et al.  Efficient query expansion with auxiliary data structures , 2006, Inf. Syst..

[8]  Stephen E. Robertson,et al.  GatfordCentre for Interactive Systems ResearchDepartment of Information , 1996 .

[9]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[10]  Karen Spärck Jones Some thoughts on classification for retrieval , 1970, J. Documentation.

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

[12]  Barry Smyth,et al.  Personalized information ordering: a case study in online recruitment , 2003, Knowl. Based Syst..

[13]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[14]  M. Leclere,et al.  Human resource management and semantic Web technologies , 2004, Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004..

[15]  Mathieu Roche,et al.  Pruning Terminology Extracted from a Specialized Corpus for CV Ontology Acquisition , 2006, OTM Workshops.

[16]  Jean-Luc Minel,et al.  Linguistic information extraction for job ads (SIRE project) , 2010, RIAO.

[17]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

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

[19]  Yannick Prié,et al.  Semantic Annotation of Documents Applied to E-Recruitment , 2006, SWAP.

[20]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[21]  Domenica Fioredistella Iezzi,et al.  Recruitment via web and information technology : a model for ranking the competences in job market , 2006 .

[22]  Peter A. Flach,et al.  Learning Decision Trees Using the Area Under the ROC Curve , 2002, ICML.

[23]  Irena Gorenak,et al.  Cross-cultural comparison of online job advertisements , 2010 .

[24]  Barry Smyth,et al.  Automated Collaborative Filtering Applications for Online Recruitment Services , 2000, AH.

[25]  Kevin Mellet,et al.  Job board toolkits: Internet matchmaking and changes in job advertisements , 2007 .

[26]  Djamel A. Zighed,et al.  Data Mining et analyse des CV : une expérience et des perspectives , 2003, EGC.

[27]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[28]  P. Bellot,et al.  Classification et segmentation de textes par arbres de décision Application à la recherche documentaire , 2001 .

[29]  Michel Leclère,et al.  The Semantic Web in e-recruitment (2004) , 2004 .

[30]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[31]  Florian Boudin,et al.  NEO-CORTEX: A Performant User-Oriented Multi-Document Summarization System , 2007, CICLing.

[32]  M. Roche,et al.  Evaluation et détermination de la pertinence pour des syntagmes candidats à la collocation , 2008 .

[33]  Juan-Manuel Torres-Moreno,et al.  Automatic Summarization System coupled with a Question-Answering System (QAAS) , 2009, ArXiv.

[34]  Mathieu Roche,et al.  Automatic Profiling System for Ranking Candidates Answers in Human Resources , 2008, OTM Workshops.

[35]  A. Bernstein,et al.  SimPack: A Generic Java Library for Similarity Measures in Ontologies , 2005 .

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

[37]  Francesco M. Donini,et al.  A Formal Approach to Ontology-Based Semantic Match of Skills Descriptions , 2003, J. Univers. Comput. Sci..

[38]  Gerard Salton,et al.  Improving retrieval performance by relevance feedback , 1997, J. Am. Soc. Inf. Sci..

[39]  Thorsten Joachims,et al.  A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization , 1997, ICML.