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@article{194819,
        author = {B. Manohar Prasad and D. Navya Kanchana and Ch. Bhavana and G. Praneetha and A. R. V. Prasad},
        title = {Movie Recommendation System: Natural Language Processing-Based Recommender System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8030-8035},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194819},
        abstract = {In the age of digital entertainment, consumers have access to a vast library of films on numerous streaming services. Users frequently find it challenging to find films that fit their interests due to this quantity. In order to overcome this difficulty, this study introduces a content-based movie recommendation system that offers tailored movie recommendations by utilizing Natural Language Processing (NLP) approaches. To calculate movie similarity, the suggested system examines metadata including cast, genres, keywords, and synopsis. Text preparation techniques such as TF-IDF vectorization, stop-word removal, and tokenization are used to convert textual data into numerical form. After that, recommendations are produced by calculating the cosine similarity between films. To offer an interactive user experience, the system is implemented in Python and made available via a web-based interface. Experimental results show that the system responds quickly and generates recommendations that are both pertinent and significant. Through the simplification of the movie discovery process, the suggested method increases user pleasure.},
        keywords = {Movie Recommendation System, Natural Language Processing, TF-IDF, Cosine Similarity, Content-Based Filtering},
        month = {March},
        }