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dc.contributor.authorzaroor, abeer $AAUP$Palestinian-
dc.contributor.authormaree, mohammed $AAUP$Palestinian-
dc.contributor.authorsabha, muath $AAUP$Palestinian-
dc.description.abstractDue to the increasing growth in online recruitment, traditional hiring methods are becoming inefficient. This is due to the fact that job portals receive enormous numbers of unstructured resumes - in diverse styles and formats - from applicants with different fields of expertise and specialization. Therefore, the extraction of structured information from applicant resumes is needed not only to support the automatic screening of candidates, but also to efficiently route them to their corresponding occupational categories. This assists in minimizing the effort required by employers to manage and organize resumes, as well as to screen out irrelevant candidates. In this paper, we present JRC - a Job Post and Resume Classification system that exploits an integrated knowledge base for carrying out the classification task. Unlike conventional systems that attempt to search globally in the entire space of resumes and job posts, JRC matches resumes that only fall under their relevant occupational categories. To demonstrate the effectiveness of the proposed system, we have conducted several experiments using a real-world recruitment dataset. Additionally, we have evaluated the efficiency and effectiveness of proposed system against state-of-the-art online recruitment systems.en_US
dc.publisherProceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)en_US
dc.titleJRC: A Job Post and Resume Classification System for Online Recruitmenten_US
Appears in Collections:Faculty & Staff Scientific Research publications

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