Improving Jobs-Resumes Classification: A Labor Market Intelligence Approach

Saúl Iván Beristain (), Rutilio Rodolfo López Barbosa () and Elena García Barriocanal ()
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Saúl Iván Beristain: Computer Science Department, Universidad de Alcalá de Henares, Spain
Rutilio Rodolfo López Barbosa: ��Computing Department, Universidad de Colima, México
Elena García Barriocanal: Computer Science Department, Universidad de Alcalá de Henares, Spain

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 04, 1509-1525

Abstract: This research proposes a framework to improve the efficiency of classification and matching of descriptions of skill on resumes with jobs vacancies using labor market intelligence over a dataset of resumes harvested from social networks. To carry out the experiments, a Kaggle dataset was downloaded containing information from the LinkedIn social network with more than 200,000 records that were later filtered and pre-processed to generate a topic model to classify the entire dataset. Later, using machine learning algorithms, prediction exercises were performed to determine the most efficient match. This model offers high percentages of efficiency when predicting the job position of a candidate of information technology (IT) areas This prediction is achieved due the reduction of categories in these areas generated by the creation of the corresponding topic model to match the resume with the job position.

Keywords: Classification; machine learning; topic model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023500013

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