Citation: Alsaif, S.A.; Sassi Hidri, M.;
Eleraky, H.A.; Ferjani, I.; Amami, R.
Learning-Based Matched
Representation System for Job
Recommendation. Computers 2022,
11, 161. https://doi.org/10.3390/
computers11110161
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and Manolis
Maragoudakis
Received: 20 October 2022
Accepted: 7 November 2022
Published: 14 November 2022
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Article
Learning-Based Matched Representation System for
Job Recommendation
Suleiman Ali Alsaif , Minyar Sassi Hidri * , Hassan Ahmed Eleraky , Imen Ferjani and Rimah Amami
Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal
University, Dammam 31441, Saudi Arabia
* Correspondence: mmsassi@iau.edu.sa
Abstract:
Job recommender systems (JRS) are a subclass of information filtering systems that aims
to help job seekers identify what might match their skills and experiences and prevent them from
being lost in the vast amount of information available on job boards that aggregates postings from
many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been
implemented, most of them failed to recommend job vacancies that fit properly to the job seekers
profiles when dealing with more than one job offer. They consider skills as passive entities associated
with the job description, which need to be matched for finding the best job recommendation. This
paper provides a recommender system to assist job seekers in finding suitable jobs based on their
resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and
measuring similarity between the job seeker’s skills and explicit features of job listing using content-
based filtering. First-hand information was gathered by scraping jobs description from Indeed from
major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job
offers were analyzed and job recommendation was made by matching skills from resumes to posted
jobs. To quantify recommendation success and error rates, we sought to compare the results of our
system to reality using decision support measures.
Keywords:
job recommender system; content-based filtering; web scrapping; job matching; machine
learning; indeed; natural language processing
1. Introduction
Online recruiting websites or job boards, such as Indeed or LinkedIn become one of
the main channels for people to find jobs. These web platforms have provided their services
for more than ten years and have saved a lot of time and money for both job seekers and
organizations who want to hire people.
To serve the constant cycle of the hiring process from the job seeker’s perspective,
many job companies have come up with solutions for providing the job board. Here, a
seeker searches for the job they would find relevant to them and apply for it. As there
are many job boards, applicants tend to use the tool that provides better services to them,
services such as writing a resume, creating a job profile, and recommending new jobs to a
job seeker.
Since the number of results returned to job seekers may be huge, they are required
to spend a significant amount of time reading and reviewing their options. That is why
traditional information retrieval (IR) techniques may not be appropriate for users.
To improve traditional IR techniques, several published works consider skills as
keywords associated with the job description, which needs to be matched for finding the
best job recommendation (e.g., [
1
,
2
]). They consider user–job dyadic data for modeling
recommendations using both content-based and collaborative filtering-based methods [
1
].
For these models, skill is not an active entity, they are used indirectly as keywords in the
job profile and user profile.
Computers 2022, 11, 161. https://doi.org/10.3390/computers11110161 https://www.mdpi.com/journal/Computers
