https://github.com/khanhnamle1994/movielens
4 different recommendation engines for the MovieLens dataset.
https://github.com/khanhnamle1994/movielens
collaborative-filtering content-based-recommendation deep-learning jupyter-notebook movielens notebooks recommender-systems
Last synced: 25 days ago
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4 different recommendation engines for the MovieLens dataset.
- Host: GitHub
- URL: https://github.com/khanhnamle1994/movielens
- Owner: khanhnamle1994
- License: mit
- Created: 2018-03-28T09:25:02.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-07-12T15:18:48.000Z (almost 6 years ago)
- Last Synced: 2025-03-29T23:09:12.411Z (about 1 month ago)
- Topics: collaborative-filtering, content-based-recommendation, deep-learning, jupyter-notebook, movielens, notebooks, recommender-systems
- Language: Jupyter Notebook
- Homepage: https://grouplens.org/datasets/movielens/
- Size: 41.9 MB
- Stars: 432
- Watchers: 15
- Forks: 185
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MovieLens Recommendation Systems
This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the [MovieLens 1M dataset](https://grouplens.org/datasets/movielens/1m/). The dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000.
Here are the different notebooks:
* [Data Processing](http://nbviewer.jupyter.org/github/khanhnamle1994/movielens/blob/master/Data_Processing.ipynb): Loading and processing the users, movies, and ratings data to prepare them for input into my models.
* [Content-Based and Collaborative Filtering](http://nbviewer.jupyter.org/github/khanhnamle1994/movielens/blob/master/Content_Based_and_Collaborative_Filtering_Models.ipynb): Using the Content-Based and Collaborative Filtering approach
* [SVD Model](http://nbviewer.jupyter.org/github/khanhnamle1994/movielens/blob/master/SVD_Model.ipynb): Using the SVD approach
* [Deep Learning Model](http://nbviewer.jupyter.org/github/khanhnamle1994/movielens/blob/master/Deep_Learning_Model.ipynb): Using the Deep Learning approach
An accompanied Medium blog post has been written up and can be viewed here: [The 4 Recommendation Engines That Can Predict Your Movie Tastes](https://medium.com/@james_aka_yale/the-4-recommendation-engines-that-can-predict-your-movie-tastes-bbec857b8223)
## Requirements
* [Python 2.7](https://www.python.org/download/releases/2.7/) or [Python 3.6](https://www.python.org/downloads/release/python-360/)
* [Jupyter Notebook](http://jupyter.org/)
## Dependencies
Choose the latest versions of any of the dependencies below:
* [pandas](https://pandas.pydata.org/)
* [numpy](http://www.numpy.org/)
* [scipy](https://www.scipy.org/)
* [matplotlib](https://matplotlib.org/)
* [sklearn](http://scikit-learn.org/stable/)
* [wordcloud](https://github.com/amueller/word_cloud)
* [searborn](https://seaborn.pydata.org/)
* [surprise](http://surpriselib.com/)
* [keras](https://keras.io/)
* [h5py](https://www.h5py.org/)
## License
MIT. See the LICENSE file for the copyright notice.