Abstract
In this paper, we describe a CV recommender system with a focus on two properties. The first property is the ability to classify candidates into roles based on automatic processing of their CV documents. The second property is the ability to recommend skills to a candidate which are not listed in their CV, but the candidate is likely to have them. Both features are based on skills extraction from a textual CV document. A spectral skill clustering is precomputed for the purpose of candidate classification, while skill recommendation is based on various similarity-based strategies. Experimental results include both automatic experiments and an empirical study, both of which demonstrate the effectiveness of the presented methods.
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Acknowledgment
The dataset for this research has been collected by EWORK (https://www.eworkgroup.com/en/contact). The authors acknowledge the support of the Croatian Science Foundation through the Reliable Composite Applications Based on Web Services (IP-01-2018-6423) research project. The Titan X Pascal used for this research was donated by the NVIDIA Corporation.
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Kurdija, A.S. et al. (2020). Candidate Classification and Skill Recommendation in a CV Recommender System. In: Xu, R., De, W., Zhong, W., Tian, L., Bai, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2020. AIMS 2020. Lecture Notes in Computer Science(), vol 12401. Springer, Cham. https://doi.org/10.1007/978-3-030-59605-7_3
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