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Authors: Iuliana Marin 1 and Hanoosh Amel 2

Affiliations: 1 Faculty of Engineering in Foreign Languages, University Politehnica of Bucharest, Splaiul Independenței 313, Bucharest, Romania ; 2 Ministry of Education, Directorate of Almuthanna Education, Muthanna, Iraq

Keyword(s): Jobs, Skills, Recruitment Platform, Recommendations, Machine Learning.

Abstract: After three years of dealing with a global medical catastrophe, our society is attempting to re-establish normalcy. While companies are still struggling to get back on track, workers have grown afraid to seek new jobs, either because they offer low pay or an uncertain schedule. The result is a disconnected environment that does not merge, even though it appears to. The proposed approach creates a suitable recommender system for those looking for jobs in data science. The first-hand information is gathered by collecting Indeed.com’s data science job listings, analysing the top talents that employers value, and generating job ideas by matching a user’s skills to openings that have been listed. This process of job suggestion would assist the user in concentrating on the positions where he has the greatest chance of succeeding rather than applying to every position in the system. With the aid of this recommendation system, a recruiter’s burden would be decreased because it lowers the qua ntity of undesirable prospects. (More)

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Paper citation in several formats:
Marin, I. and Amel, H. (2023). Web Platform for Job Recommendation Based on Machine Learning. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-647-7; ISSN 2184-4895, SciTePress, pages 676-683. DOI: 10.5220/0011993600003464

@conference{enase23,
author={Iuliana Marin and Hanoosh Amel},
title={Web Platform for Job Recommendation Based on Machine Learning},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2023},
pages={676-683},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011993600003464},
isbn={978-989-758-647-7},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Web Platform for Job Recommendation Based on Machine Learning
SN - 978-989-758-647-7
IS - 2184-4895
AU - Marin, I.
AU - Amel, H.
PY - 2023
SP - 676
EP - 683
DO - 10.5220/0011993600003464
PB - SciTePress