Okay, let's actually build a full-blown recommender system that can look at all the behavior information of everybody in the system, and what movies they rated, and use that to actually produce the best recommendation movies for any given user in our dataset. Kind of amazing and you'll be surprised how simple it is. Let's go!
Let's begin using the ItemBasedCF.ipynb file and let's start off by importing the MovieLens dataset that we have. Again, we're using a subset of it that just contains 100,000 ratings for now. But, there are larger datasets you can get from GroupLens.org-up to millions of ratings; if you're so inclined. Keep in mind though, when you start to deal with that really big data, you're going to be pushing the limits of what you can do in a single machine and what Pandas can handle. Without...
