Bringing Order to the Job Market
Emmanuel Malherbe, Mario Cataldi, Andrea Ballatore · 2015
E-recruitment uses a range of web-based technologies to find, evaluate, and hire new personnel for organizations. A crucial challenge in this arena lies in the categorization of job offers: candidates and operators often explore and analyze large numbers of offers and profiles through a set of job categories. To date, recruitment organizations define job categories top-down, relying on standardized vocabularies that often fail to capture new skills and requirements that emerge from dynamic labor markets. In order to support e-recruitment, this paper presents a dynamic, bottom-up method to automatically enrich and revise job categories. The method detects novel, highly characterizing terms in a corpus of job offers, leading to a more effective categorization, and is evaluated on real-world data by Multiposting (http://www.multiposting.fr/en), a large French e-recruitment firm.