About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings

Research Article

CVO: Curriculum Vitae Optimization by Recommending Keywords to Undergraduate Students

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-33614-0_19,
        author={Cibele Santos and Fabr\^{\i}cio G\^{o}es and Carlos Martins and Felipe da Cunha},
        title={CVO: Curriculum Vitae Optimization by Recommending Keywords to Undergraduate Students},
        proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings},
        proceedings_a={BDTA},
        year={2023},
        month={5},
        keywords={Clustering Curriculum Vitae Optimization Keywords Recommendation},
        doi={10.1007/978-3-031-33614-0_19}
    }
    
  • Cibele Santos
    Fabrício Góes
    Carlos Martins
    Felipe da Cunha
    Year: 2023
    CVO: Curriculum Vitae Optimization by Recommending Keywords to Undergraduate Students
    BDTA
    Springer
    DOI: 10.1007/978-3-031-33614-0_19
Cibele Santos1,*, Fabrício Góes2, Carlos Martins3, Felipe da Cunha4
  • 1: Post-Graduation Program in Electrical Engineering, Pontifícia Universidade Católica de Minas Gerais, Belo Horizonte
  • 2: Informatics Department
  • 3: Post-Graduation Program in Informatics, Pontifícia Universidade Católica de Minas Gerais, Belo Horizonte
  • 4: Department of Computer Science, Pontifícia Universidade Católica de Minas Gerais, Belo Horizonte
*Contact email: cibelesimoesoliveira@gmail.com

Abstract

Candidate selection platforms have been widely used in companies that seek agility in the process of hiring. Candidates who do not meet the requirements of a job vacancy are disqualified in the first step, called screening. This stage has been automated due to the large volume of curriculum vitae (CV) of candidates per vacancy, particularly for internship vacancies. As a consequence, candidates receive little to none feedback and do not know how to improve/optimize their CVs for new applications. The goal of this paper is to realize the curriculum vitae optimization (CVO) process for internship vacancies by implementing a recommendation system that given an undergraduate student CV, it suggests the addition of relevant keywords, taking into account the student’s undergraduate course. This system is implemented based on the clustering of CVs keywords, from an internship recruitment private company database, into profile groups which are linked to internship vacancies. The experimental results showed that recommendations improved students CVs similarity (competitiveness within a specific field) from 18.83%, with 3 keywords recommendation, up to 50.67%, with 10 words.

Keywords
Clustering Curriculum Vitae Optimization Keywords Recommendation
Published
2023-05-26
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-33614-0_19
Copyright © 2021–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL