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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: * Data Processing: Loading and processing the users, movies, and ratings data to prepare them for input into my models. * Content-Based and Collaborative Filtering: Using the Content-Based and Collaborative Filtering approach * SVD Model: Using the SVD approach * Deep Learning Model: 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



Choose the latest versions of any of the dependencies below: * pandas * numpy * scipy * matplotlib * sklearn * wordcloud * searborn * surprise * keras * h5py


MIT. See the LICENSE file for the copyright notice.

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