Hands-On Recommendation Systems with Python

Summary

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 ...