In this chapter, we have covered a lot of topics that will help us to build powerful collaborative filters. We took a look at clustering, a form of unsupervised learning algorithm that could help us to segregate users into well defined clusters. Next, we went through a few dimensionality reduction techniques to overcome the curse of dimensionality and improve the performance of our learning algorithms.
The subsequent section dealt with supervised learning algorithms, and finally we ended the chapter with a brief overview of various evaluation metrics.
The topics covered in this chapter merit an entire book and we did not analyze the techniques in the depth usually required of machine learning engineers. However, what we have learned in this chapter should be sufficient to help us build and understand collaborative filters, which is one of the main objectives of this book...