SmartElect: An AI-Based Personalized Elective Course Recommendation System Using TF-IDF and Cosine Similarity

Author(s)	Ms. Jafrin Sultana, Ms. Sneha Popat Mahadik, Ms. Vishakha Hanmant Sutar, Prof.Madhura M. Raste
Country	India
Abstract	The rapid expansion of online learning platforms like Coursera, Udemy, and edX has increased access to diverse elective courses, but also created challenges in selecting the most suitable options. To address this, this paper proposes SmartElect, an AI-powered elective course recommendation system that delivers personalized suggestions based on user skills and interests. The system utilizes TF-IDF vectorization and a content-based filtering approach with cosine similarity to match user input with relevant courses. It is evaluated using Precision, Recall, and NDCG, showing effective recommendation performance. Built with a React frontend and Flask backend, the system also includes an AI chatbot and career roadmap features, helping students make informed and efficient course selection decisions.
Keywords	Machine Learning, Term Frequency–Inverse Document Frequency, Cosine Similarity, Content-Based Filtering, Course Recommendation System
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-13

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International Journal For Multidisciplinary Research