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@article{176575,
        author = {Anushifa J and Akshaya Lakshmi R and Ashvini Uma A},
        title = {AI Powered Personalized Course Recommender},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5962-5965},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176575},
        abstract = {This paper presents an AI-powered Course Recommender System designed to help learners find relevant online courses based on their interests. The system utilizes machine learning algorithms, including TF-IDF with Cosine Similarity and K-Nearest Neighbors (KNN), to recommend courses by analyzing course titles, categories, and user input. A Flask-based web application was developed to offer a user-friendly interface for course search and recommendations. The proposed model ensures improved accuracy in recommendations by integrating both content-based filtering and user similarity approaches. This project aims to assist students, professionals, and lifelong learners in identifying valuable learning resources efficiently.},
        keywords = {Course Recommendation, Cosine Similarity, K-Nearest Neighbors, Machine Learning, Natural Language Processing, TF-IDF.},
        month = {April},
        }