Development of a recommender system to choose a university degree program

Abstract

In the evolving landscape of higher education, students face challenges in selecting the most suitable university and specialization based on their academic backgrounds. This project develops a hybrid recommendation system that integrates machine learning models to provide personalized specialization and university recommendations. The system incorporates Random Forest and XGBoost for content-based filtering, leveraging students’ academic profiles, such as CGPA, GRE scores, and prior work and research experience, to identify optimal choices. Additionally, memory-based collaborative filtering using cosine similarity is implemented to generate recommendations based on user similarity. To enhance adaptability, the system incorporated a dynamic α-weighting hybrid recommendation system that adjusts the influence of content-based and collaborative filtering based on model variance, improving recommendation accuracy. This approach ensures a personalized decision-making tool that aids students in navigating their higher education choices effectively.