ORCID

Abstract

A dynamic, constantly shifting labor market creates enormous job postings, overwhelming candidates and making it difficult for businesses to find quality candidates. It is also hard for job seekers to find suitable jobs. Addressing these issues, machine learning-driven job recommender systems have recently become an essential tool using predictivemodels to improve the match between jobs and candidates. A hybrid design that combine scollaborative filtering with content-based filtering andadds contextual information like geographic location,industry trends, and user behavioural data can enhance the accuracy and relevance of recommendations. This paper reviews and critically analyzes contemporary jobre commender system techniques. The focus is on hybrid recommendation models and the integration ofalgorithmic approaches, indicating their strengths and weaknesses. This review also looks into the evaluation metrics like precision, recall, normalized discounted cumulative gain (NDCG), Root Mean Square Error(RMSE), and Mean Absolute Error (MAE). To provide an overall perspective of the various approaches employed and the performance trade-offs inherent therein, this paper hopes to shed some light on the optimization of job recommendation systems for better effectiveness and user satisfaction.

Publication Date

2025-07-30

Publication Title

International Journal on Robotics, Automation and Sciences

Volume

72

Issue

2

ISSN

2682-860X

Acceptance Date

2025-05-10

Deposit Date

2026-07-08

Funding

There are no funding agencies supporting the research work.

Keywords

Recommender System, Machine Learning, Hybrid-based, Job Recommender, Evaluation Metrics, comprehensive review

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