Candidate Classification and Skill Recommendation in a CV Recommender System
| Autor: | Adrian Satja Kurdija, Goran Delac, Sinisa Srbljic, Lucija Sikic, Petar Afric, Boris Plejic, Marin Silic, Klemo Vladimir |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Property (programming) Computer science business.industry 05 social sciences Automatic processing Recommender system Machine learning computer.software_genre 01 natural sciences Spectral clustering Empirical research Recommender systems Skill recommendation Classification Similarity (psychology) 050501 criminology Artificial intelligence business Cluster analysis computer 0505 law 0105 earth and related environmental sciences |
| Zdroj: | Artificial Intelligence and Mobile Services – AIMS 2020 ISBN: 9783030596040 AIMS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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