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@article{205816,
        author = {Meghana J L and Mithra N Gowda and Dr. Vivekanand S Gogi},
        title = {Hybrid Movie Recommendation System Combining Content-Based and Collaborative Filtering},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8327-8333},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205816},
        abstract = {This paper presents a hybrid movie recommendation system that integrates content-based filtering and collaborative filtering techniques to provide personalized and accurate movie suggestions. The system leverages TF-IDF vectorization to analyze movie metadata such as genres, overviews, and cast information, while employing Singular Value Decomposition (SVD) to model user-item interactions from historical ratings. A weighted hybrid approach combines both methods, allowing dynamic adjustment of recommendation influence through a tunable parameter. Implemented as a Flask-based web application with RESTful APIs, the system demonstrates real-time recommendation capabilities and scalability. Experimental results on a dataset containing over 45,000 movies and 100,000 ratings show improved recommendation relevance and diversity compared to standalone approaches. The proposed system also enhances user experience by offering transparency in scoring through separate content and collaborative contributions. Furthermore, its modular architecture ensures extensibility, enabling future integration of advanced techniques and larger datasets for improved performance.},
        keywords = {Hybrid Recommendation System, Content-Based Filtering, Collaborative Filtering, TF-IDF, Singular Value Decomposition (SVD), Machine Learning, Recommender Systems, Flask Web Application, Personalization},
        month = {June},
        }