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(AIMS 2020) Building Vector Representations for Candidates and Projects in a CV Recommender System

About This Webinar

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: adrian.kurdija@fer.hr, petar.afric@fer.hr, lucija.sikic@fer.hr, boris.plejic@ericsson.com, marin.silic@fer.hr, goran.delac@fer.hr, klemo.vladimir@fer.hr, sinisa.srbljic@fer.hr

Who can view: Everyone
Webinar Price: Free
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Marin Silic is an Associate Professor at the University of Zagreb, Faculty of Electrical Engineering and Computing. He received his Ph.D. in Computer Science from the University of Zagreb Faculty of Electrical Engineering and Computing in 2013. His research interests span distributed systems, software engineering, data mining, recommender systems, machine learning and artificial intelligence in general. He is a member of the IEEE.
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