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@article{197501,
        author = {P. Narendra and P. Baby and S. Jyoshna and K. Karthik and K. Kalyani},
        title = {ARTICLE RECOMMENDER SYSTEM: MACHINE LEARNING AND NLP-BASED PERSONALIZED RECOMMENDATION ENGINE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13873-13879},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197501},
        abstract = {The rapid growth of digital publishing platforms has resulted in significant information overload, making it difficult for users to efficiently discover relevant content. This research presents a Machine Learning and Natural Language Processing (NLP)-based personalized Article Recommender System. The proposed system utilizes text preprocessing techniques, TF-IDF vectorization for feature extraction, K-Nearest Neighbour (KNN) algorithm for similarity-based candidate selection, and cosine similarity for ranking relevant articles. A user interest profile is generated based on reading history, enabling personalized recommendations. Experimental results demonstrate that the system effectively identifies relevant articles and improves content discovery efficiency. The proposed framework provides ascalable and computationally efficient solution for personalized recommendation systems.},
        keywords = {Article Recommendation, NLP, TF-IDF, KNN, Cosine Similarity, Machine Learning, Personalized Recommendation},
        month = {April},
        }