This project provides a recommendation system for movies based on Non-negative Matrix Factorization (NMF). The processing steps are:
- Text preprocessing (Using spacy, nltk)
- Vectorization (Using Tf-Idf Vectorization)
- Non Negative Matrix Multiplication
- Calculation of movie similarity from final feature matrix
The project includes several analyses to recommend movies to users:
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NMF-based recommendation: The script uses Non-negative Matrix Factorization (NMF) to factorize the ratings matrix into user and movie latent factors. These factors can be used to generate personalized movie recommendations for a given user.
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Movie similarity analysis: The script also calculates the similarity between movies based on their latent factors. This can be used to recommend similar movies to a given user, or to analyze the relationships between different movies.
Dataset is prepared from CMU Movie Summary Corpus, a collection of 42,306 movie plot summaries
'movie.metadata.tsv': includes metadata on movies such as title, genre 'plot_summaries.txt': contains summaries of movie plots
This project is licensed under the MIT License. See the LICENSE file for more details.
Contributions are welcome! If you want to contribute to this project, please fork the repository and submit a pull request with your changes.