Movie Recommendation System Using Hybrid Based Method

The rise of digital platforms has increased user engagement and satisfaction through personalized recommendation systems. The research paper presents a hybrid approach to movie recommendation, which uses content-based filtering and collaborative filtering techniques to mitigate the cold start problem, data sparsity, and scalability issues. The proposed system will utilize methodologies from machine learning, such as cosine similarity and vectorization, and the K-Nearest Neighbors (KNN) algorithm, for recommending movies based on their precise and personalized values. The dataset TMDB5000 will then be used for two datasets involving movie features, cast, and crew information while preprocessing techniques in cleaning, feature extraction, and merging are undertaken to ensure robust foundation for the model building. The hybrid model uses the bag-of-words technique to vectorize and compute cosine similarity to determine movies' relationships in a multidimensional space. The KNN algorithm then refines the collaborative filtering process by the clustering of user preferences and makes recommendations using nearest-neighbor analysis. Our current approach overcomes these weaknesses by combining both content-based and collaborative methods, resulting in a system which handles diverse user preferences while maintaining high recommendation accuracy. As the evaluation of precision, recall, F1-score, and mean squared errors further validates that this system performance is good and also scalable and efficient for processing large datasets for which it explains some practical aspects related to implementation in real time. The future work includes deep learning models like neural collaborative filtering to further improve semantic understanding and recommendation accuracy. This paper addresses the current system's limitations by proposing a novel, efficient, and user-centric hybrid approach, which can be extended to other domains, such as e-commerce, music, and book recommendations.