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.
Authors: Adrian S Kurdija, Petar Afric and Lucija Šikić (University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia); Boris Plejić (Ericsson Nikola Tesla, Zagreb, Croatia); Marin Šilić (University of Zagreb, Croatia); Goran Delac, Klemo Vladimir and Sinisa Srbljic (University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia)
Email: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org