Personalized job recommender system using content-based filtering for UiTM Machang graduates
Traditional methods for finding jobs include inefficiencies in irrelevant job listing and inability to provide personalized recommendations based on the user's specific skills, career goals, and preferences. The Personalized Job Recommender System is designed to help graduates from UiTM Machang...
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| Format: | Student Project |
| Language: | en |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/121612/1/121612.pdf https://ir.uitm.edu.my/id/eprint/121612/ |
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| Summary: | Traditional methods for finding jobs include inefficiencies in irrelevant job listing and inability to provide personalized recommendations based on the user's specific skills, career goals, and preferences. The Personalized Job Recommender System is designed to help graduates from UiTM Machang face the challenges of an overwhelming job market. In an attempt to solve these challenges, Personalized Job Recommender System integrates recommendation techniques in developing a system that offers personalized job suggestions. The system is developed on the Agile framework, an iterative and collaborative methodology that emphasizes user feedback and continuous improvement. The system has a streamlined interface that allows users to create profiles, receive job recommendations, and apply for positions with ease . The System Usability Scale (SUS) and User Acceptance Testing (UAT) are used to evaluate the system’s usability because they are well-known and trusted methods for measuring user experience. Early feedback has been very promising and assures that this system holds immense potential for improving job searching to lower the manual searches and raise the accuracy of the job matching for increased employment success rate among the graduates to make entry into the workforce easier. The Personalized Job Recommender System makes the job searching process easy and at the same time, contributes toward closing the gap between graduates and suitable employment chances. Further development will be directed to extend the system for wider coverage, including other industries and also experienced workers, to make it scalable and relevant in a continuously changing labor market. Advanced features, such as AI-driven recommendation algorithms, integration with real-time job market APIs, and mobile application support, are planned to improve personalization and accessibility. |
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