Academic Recommender System Utilizing a Genetic Algorithm - dela Cruz, Sandrex Mamuad (International Journal of Computing Sciences Research; Vol 9 (2025): Volume 9, 2025)
Purpose – The study aims to develop an academic recommender system for various higher education institutions, particularly in Ilocos Norte. A recommendation model will be trained via TensorFlow with the use of various datasets that will be collected from the participating Higher Education Institution (HEI). The system will utilize a genetic algorithm to generate personalized recommendations tailored to the students' profiles. The goal is to assess the performance of the algorithm in giving recommendations to the users, as well as to develop a recommendation system that can be used by the students. Method – This study will employ a combination of quantitative and qualitative methods. The qualitative method will review the current systems, technologies, and existing methods that are utilized in developing a recommender system, specifically in the field of academe. The quantitative aspect involves data analysis and modeling to create degree program recommendations. Conclusion – The study focuses on the evaluation of the genetic algorithm in giving recommendations. This will also explore and compare the said algorithm with the existing systems that use other techniques and algorithms. Furthermore, it will lead to the development of an academic recommender system which can be used by the students to make sure that they are on their right path and will also guide them in choosing the right degree that suits their profile. Recommendations – This study strongly recommends the adoption of the proposed system in educational sectors. The implementation of the study will help maximize the success rate of students in their academic pursuits as the system will serve as their guide in their academic journey. Practical Implications – The academic recommender system will benefit not only the students and the higher education involved but also the future researchers who will conduct a similar study, as it will contribute valuable insight into the field of recommender systems.