Context-Aware Job Recommender System - Azri, Muhammad Haziq Fikri Bin; Haw, Su-Cheng; Ng, Kok-Why; Saad, Mohamad Firdaus Mat (JOIV : International Journal on Informatics Visualization, 2025)
Context-aware recommendation systems have emerged as essential to interactive web content and online job search. Primarily, since so many job offers are published on different online platforms, it can make the users take some time to find good opportunities that match exactly what they are looking for, as well as countless qualified candidates and other characteristics within that context, such as temporality. This comes as no surprise, as many practitioners and researchers have resorted to machine learning to create context-aware job recommendation systems that cater not only to job seekers. In this comparative paper, we have analyzed various machine-learning models for job recommendation systems. Four fundamental pillars are considered: accuracy, scalability, interpretability, and computational efficiency. This paper also studies the extent to which these models are contextual (e.g., how well they can model factors due to user preferences, job requirements, location, industry evolution, and temporality.) and can be used as a recommendation system. This study uses real-world employment data from actual employment statistics (through fixed-period analysis), professional networking platforms, and online job market platforms. The study does so purposefully to be comprehensive because it believes the lessons from remote work are generalizable. Still, the data is from a wide variety of job sectors, job positions, and locations. The group created a test environment for constructing and testing machine learning algorithms. Collaborative filtering, content-based, matrix factorization, deep learning, and many other hybrid approaches have obtained better results. This study was performed on Python with sci-kit-learn, pandas, and NumPy. The proposed system is a context-aware job recommender system that employs many machine learning algorithms to personalize job recommendations concerning user preferences and contextual information such as job location, industry status, and temporal dynamics. The findings underscore the importance of choosing machine learning models that are well-suited for job recommendation systems on a case-by-case basis. This comparative study intends to add to the art by providing algorithmic proof and practical advice to properly leverage machine learning models proposed in a naturalistic, messy setting of context-aware job recommendation systems.