Description of Invention
Amidst the dynamic changes in higher education in Malaysia, students sometimes feel inundated by the numerous university choices at their disposal. The objective of this project is to tackle this issue by creating a university Recommender Engine that utilizes sophisticated recommender system approaches to offer individualized institution suggestions. The project will employ the TF-IDF (Term Frequency-Inverse Document Frequency) Cosine Similarity algorithm to examine user preferences and align them with the profiles of different colleges. The research topic centers around the challenge students have while trying to determine the most suitable schools, due to insufficient knowledge and subjective biases. The project's objective is to create a reliable and effective recommendation system capable of providing precise and varied university recommendations customized to match each student's specific interests. The process entails gathering extensive data on colleges, encompassing information on courses, prices, scholarships, and locations. This data is then converted into numerical vectors to facilitate similarity computing. The system will utilize the TF-IDF Cosine Similarity method to compute the similarity between user preferences and university profiles. Based on this calculation, the system will rank the institutions appropriately. The project's main objective is to optimize the institution selection process while improving the whole student experience via data-driven and individualized coaching.