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4 different recommendation engines for the MovieLens dataset.
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MovieLens Recommendation Systems

This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the MovieLens 1M dataset. 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:

An accompanied Medium blog post has been written up and can be viewed here: The 4 Recommendation Engines That Can Predict Your Movie Tastes



Choose the latest versions of any of the dependencies below:


MIT. See the LICENSE file for the copyright notice.

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