Movie Recommender Sample Project
This is a sample for recommenders. Explore the gallery to see other examples.
This sample code shows how to build, evaluate, and deploy a recommender model for movies. You could use this model to power "Recommended for you" or suggest "Similar Movies" features.
Get started
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Before you begin, make sure you have installed GraphLab Create, a Python package for machine learning.
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Download and extract the example code to a directory on your machine, or clone it with the following command:
git clone http://github.com/turi-code/sample-movie-recommender cd sample-movie-recommender
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While in the
sample-movie-recommender
directory, run the following script to download the sample project data:
python download_data.py
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Making sure you are working in a Python environment with GraphLab Create installed, run the
movie_recommender.py
script to build and explore the recommender model on your machine:python -i movie_recommender.py
The
-i
flag causes Python to drop into an interactive interpreter after the script executes.Alternatively, you can also run the provided IPython Notebook:
ipython notebook movie_recommender.ipynb
Once the model has been created, a browser window should open to let you explore and interact with your recommender model:
Learn More and Next Steps
Once you have the sample project running, you can try the following:
To find out more about building recommender models, check out the user guide or API documentation.
Troubleshooting
If you are having trouble, please create a Github Issue or start a discussion on the user forum.
Acknowledgements
The MovieLens dataset was collected by the GroupLens Research Project at the University of Minnesota.