Deep Hybrid System for Personalized Movie Recommendations

A proposal framework is a sort of programming application or calculation intended to recommend things, for example, items, films, melodies, articles to clients in light of their inclinations, ways of behaving, or comparable client's exercises. This paper presents an original crossover suggestion framework that coordinates content-based and cooperative separating approaches utilizing profound learning procedures to improve film proposals. Our model merges the metadata of movies, including genres, cast, and crew from the Movie Lens dataset with user ratings to construct a comprehensive feature set. We employ a Term Frequency-Inverse Document Frequency (TFIDF) vectorizer to extract content-based features and utilize Singular Value Decomposition (SVD) to derive collaborative filtering features, thereby addressing both user preferences and item characteristics. We further enhance the model by concatenating these features into a unified representation, which is then processed through a deep neural network to predict movie ratings. The network architecture consists of multiple dense layers with dropout regularization to prevent overfitting, ensuring robustness in learning complex user-item interactions. We evaluate our model on a standard dataset, focusing on mean squared error (MSE) as the performance metric to assess accuracy. The results demonstrate the effectiveness of our hybrid approach in providing precise recommendations by leveraging both the semantic content of movies and the historical interactions of users, thereby outperforming traditional methods that rely on singular recommendation strategies. This research contributes to the recommendation system community by showcasing a scalable and efficient method to improve