A novel approach for job matching and skill recommendation using transformers and the O*NET database

Today we have tons of information posted on the web every day regarding job supply and demand which has heavily affected the job market. The online enrolling process has thus become efficient for applicants as it allows them to present their resumes using the Internet and, as such, simultaneously to numerous organizations. Online systems such as Monster.com, OfferZen, and LinkedIn contain millions of job offers and resumes of potential candidates leaving to companies with the hard task to face an enormous amount of data to manage to select the most suitable applicant. The task of assessing the resumes of candidates and providing automatic recommendations on which one suits a particular position best has, therefore, become essential to speed up the hiring process. Similarly, it is important to help applicants to quickly find a job appropriate to their skills and provide recommendations about what they need to master to become eligible for certain jobs. Our approach lies in this context and proposes a new method to identify skills from candidates' resumes and match resumes with job descriptions. We employed the O*NET database entities related to different skills and abilities required by different jobs; moreover, we leveraged deep learning technologies to compute the semantic similarity between O*NET entities and part of text extracted from candidates' resumes. The ultimate goal is to identify the most suitable job for a certain resume according to the information there contained. We have defined two scenarios: i) given a resume, identify the top O*NET occupations with the highest match with the resume, ii) given a candidate's resume and a set of job descriptions, identify which one of the input jobs is the most suitable for the candidate. The evaluation that has been carried out indicates that the proposed approach outperforms the baselines in the two scenarios. Finally, we provide a use case for candidates where it is possible to recommend courses with the goal to fill certain skills and make them qualified for a certain job.

Authors

Institutions

Publication Details

Journal
Big Data Research
Published
2025-02-01
DOI
https://doi.org/10.1016/j.bdr.2025.100509
Citations
6
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
8.92

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A novel approach for job matching and skill recommendation using transformers and the O*NET database

Antonello Meloni, Diego Reforgiato Recupero, Rubén Alonso, Danilo Dessı̀
6 citations
Big Data Research
Distributed and Parallel Computing Systems
8.92
article

A novel approach for job matching and skill recommendation using transformers and the O*NET database

Antonello Meloni, Diego Reforgiato Recupero, Rubén Alonso, Danilo Dessı̀
article en
6 citations

Abstract

Today we have tons of information posted on the web every day regarding job supply and demand which has heavily affected the job market. The online enrolling process has thus become efficient for applicants as it allows them to present their resumes using the Internet and, as such, simultaneously to numerous organizations. Online systems such as Monster.com, OfferZen, and LinkedIn contain millions of job offers and resumes of potential candidates leaving to companies with the hard task to face an enormous amount of data to manage to select the most suitable applicant. The task of assessing the resumes of candidates and providing automatic recommendations on which one suits a particular position best has, therefore, become essential to speed up the hiring process. Similarly, it is important to help applicants to quickly find a job appropriate to their skills and provide recommendations about what they need to master to become eligible for certain jobs. Our approach lies in this context and proposes a new method to identify skills from candidates' resumes and match resumes with job descriptions. We employed the O*NET database entities related to different skills and abilities required by different jobs; moreover, we leveraged deep learning technologies to compute the semantic similarity between O*NET entities and part of text extracted from candidates' resumes. The ultimate goal is to identify the most suitable job for a certain resume according to the information there contained. We have defined two scenarios: i) given a resume, identify the top O*NET occupations with the highest match with the resume, ii) given a candidate's resume and a set of job descriptions, identify which one of the input jobs is the most suitable for the candidate. The evaluation that has been carried out indicates that the proposed approach outperforms the baselines in the two scenarios. Finally, we provide a use case for candidates where it is possible to recommend courses with the goal to fill certain skills and make them qualified for a certain job.

Big Data ResearchVol. 39
University of Cagliari (IT), University of Sharjah (AE), R2M Solution (Italy) (IT), Centre for Automation and Robotics (ES), Universidad Politécnica de Madrid (ES)
Horizon 2020 Framework Programme
Decent work and economic growth
Openalex Percentile: Top 3%
Distributed and Parallel Computing Systems
8.92
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.