Building Vector Representations for Candidates and Projects in a CV Recommender System

Autor:	Goran Delac, Boris Plejic, Adrian Satja Kurdija, Petar Afric, Klemo Vladimir, Marin Silic, Lucija Sikic, Sinisa Srbljic
Rok vydání:	2020
Předmět:	Set (abstract data type) Jaccard index Information retrieval Computer science Word representation Representation (systemics) LSI GloVe Recommender systems Nearest neighbors Recommender system Latent semantic indexing
Zdroj:	Artificial Intelligence and Mobile Services – AIMS 2020 ISBN: 9783030596040 AIMS
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1887be785587a1a7d67f2790e66c3e4a https://doi.org/10.1007/978-3-030-59605-7_2 Zobrazit plný text záznamu