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Job seeking and recruiting processes have drastically changed during the past decade. Today’s companies are exploiting online technology (job portals, corporate websites, and so on) to make job advertisements reach an ever-growing audience. However, this advantage can create a higher post-processing burden for recruiters, who must sort through the huge amount of résumés and curricula vitae received, often expressed in different languages and formats. Similarly, job seekers spend considerable time filtering job offers and restructuring their résumés to effectively communicate their strong points and address the job requirements.
Consequently, job recruiters and seekers often use various special-purpose tools, such as job aggregators (including www.jobrapido.com and www.indeed.com)1 and social networks (including www.linkedin.com, www.glassdoor.com, and www.jackalopejobs.com).2 To further optimize selection processes with respect to processing time and accuracy, job portals such as Monster (www. monster.com) and Jobnet (www.jobnetchannel.com) have started to develop advanced search engines to automatically sort résumés based on job offer requirements. These approaches could exploit, among others, supervised and unsupervised learning, software agents, and genetic algorithms. 3–8 Nonetheless, creating such tools is a complex task that requires identifying which variables influence the user’s final choice
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