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@article{166368,
        author = {S Aishwarya Rao and Dr. Goldi Soni},
        title = {Intelligent Movie Recommendations System with Collaborative Filtering and Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {944-950},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166368},
        abstract = {Watching movies and TV shows has become more convenient with the introduction of streaming services. Over time, the film industry has experienced rapid growth. It's difficult for users to decide what to watch these days because there is so much content available. Systems for recommending movies have been created to help consumers choose movies according to their personal tastes. This streamlines and entertains the selection process. These systems use a number of techniques to provide users with tailored recommendations. One of the most popular techniques is collaborative filtering, which suggests movies to users based on their viewing habits and past selections. An additional technique is content-based filtering, which makes use of movie characteristics such as genre, stars, and directors to recommend other films that share those characteristics. Hybrid methods that combine the two approaches have also been developed to offer recommendations that are more accurate. It highlights how important personalization is to recommendation systems because it increases user satisfaction and engagement.},
        keywords = {Collaborative Filtering, Cosine Similarity, SVD, RMSE, Recommender system},
        month = {July},
        }