A Flask-based web application that recommends movies based on user input. The recommendation system is built using the TMDB dataset and employs TF-IDF vectorization and cosine similarity to find similar movies. The system uses natural language processing techniques to analyze the text features of movies and identify similarities.
Recommends movies based on text similarity using TF-IDF and cosine similarity.
Type in a movie title to get recommendations.
Built with Flask and Jinja2 templates.
Easily extendable for more complex recommendation algorithms.
git clone https://github.com/your-username/movie-recommender-system.git
cd movie-recommender-system
python app.py
The movie data is sourced from the TMDB dataset, which contains metadata for thousands of movies.
The overview, genres, keywords, cast, and director of each movie are combined and processed into a single text feature.
The text features are converted into numerical vectors using TF-IDF.
The system calculates the cosine similarity between movie vectors to find the most similar movies.