So, go give it a try! See if you can improve on our initial results there. There's some simple ideas there to try to make those recommendations better, and some much more complicated ones too. Now, there's no right or wrong answer; I'm not going to ask you to turn in your work, and I'm not going to review your work. You know, you decide to play around with it and get some familiarity with it, and experiment, and see what results you get. That's the whole point - just to get you more familiar with using Python for this sort of thing, and get more familiar with the concepts behind item-based collaborative filtering.

We've looked at different recommender systems in this chapter-we ruled out a user-based collaborative filtering system and dove straight in to an item-based system. We then used various functions from pandas to generate and refine our results, and I hope you've seen the power of pandas here.

In the next chapter, we'll take a look at more advanced data mining and machine learning techniques including K-nearest neighbors. I look forward to explaining those to you and seeing how they can be useful.