Recommender System for Smart University Registration System
DOI:
https://doi.org/10.59994/ajbtme.2025.1.1Keywords:
Recommender Systems, University Courses, Smart University System, Collaborative Filtering, Content-Based Filtering, Educational TechnologyAbstract
Recommender Systems (RSs) have gained significant traction across industries such as e-commerce and digital media; however, their use in academic course recommendation remains relatively underdeveloped. This paper explores the design and implementation of a RS tailored for a smart university registration system, with the goal of streamlining the course selection process for undergraduate students. By analysing similarities in academic plans, the system delivers personalized course suggestions, facilitating smoother pre-registration and enriching the academic journey. It employs a combination of collaborative filtering, content-based filtering, and hybrid techniques to generate precise and relevant recommendations. Designed for seamless integration with existing university infrastructure, the system prioritizes scalability, ease of use, and ethical data practices. Its adoption is expected to enhance student satisfaction, retention, and academic success, while also alleviating the burden on academic advisors and administrative personnel. The research addresses key challenges including data availability, interface usability, and integration complexity, offering practical advancements in the realm of educational technology.
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