This is the sixth post of a project on collaborative filtering based on the MovieLens 100K dataset. The remainder of this post is straight out of a Jupyter Notebook file you can download here. You can also see it here on GitHub.

This is the fifth post of a project on collaborative filtering based on the MovieLens 100K dataset. The remainder of this post is straight out of a Jupyter Notebook file you can download here. You can also see it here on GitHub.

Previously, I showed how to do matrix factorization …

This is the fouth post of a project on collaborative filtering based on the MovieLens 100K dataset. The remainder of this post is straight out of a Jupyter Notebook file you can download here. You can also see it here on GitHub.

Previously, I showed how to use similarity-based approaches …

This is the third post of a project on collaborative filtering based on the MovieLens 100K dataset. The remainder of this post is straight out of a Jupyter Notebook file you can download here. You can also see it here on GitHub.

Now that we've established some simple baseline models …

This is the second post of a project on collaborative filtering based on the MovieLens 100K dataset. The remainder of this post is straight out of a Jupyter Notebook file you can download here. You can also see it here on GitHub.

This is the first post of a project on the MovieLens dataset to learn about collaborative filtering algorithms. Here, I do an exploratory data analysis to see what the data looks like. The remainder of this post is straight out of a Jupyter Notebook file you can download here. You …

To use the map above, select a name from the dropdown list (you should be able to type a name if you don't want to scroll), then drag the slider to move in time between the years 1910 and 2014 …