Abstract
We describe a CV recommender system built for the purpose of connecting candidates with projects that are relevant to their skills. Each candidate and each project is described by a textual document (CV or a project description) from which we extract a set of skills and convert this set to a numeric representation using two known models: Latent Semantic Indexing (LSI) and Global Vectors for Word Representation (GloVe) model. Indexes built from these representations enable fast search of similar entities for a given candidate/project and the empirical results demonstrate that the obtained l2 distances correlate with the number of common skills and Jaccard similarity.
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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). Building Vector Representations for Candidates and Projects 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_2
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