This brings us to the end of our discussion on collaborative filters. In this chapter, we built various kinds of user-based collaborative filters and, by extension, learned to build item-based collaborative filters as well.
We then shifted our focus to model-based approaches that rely on machine learning algorithms to churn out predictions. We were introduced to the surprise library and used it to implement a clustering model based on kNN. We then took a look at an approach to using supervised learning algorithms to predict the missing values in the ratings matrix. Finally, we gained a layman's understanding of the singular-value decomposition algorithm and implemented it using surprise.
All the recommenders we've built so far reside only inside our Jupyter Notebooks. In the next chapter, we will learn how to deploy our models to the web, where they can be used...
