We will now explore a different kind of unsupervised learning technique that, in our example, is capable of working with data that reflects a given group of users' preferences for particular pieces of content. This section will introduce new concepts around network architecture and probability distributions, as well as how they can be used in practical implementations of recommendation systems, specifically for recommending films that a given user may find interesting.
Building an RBM for Netflix-style collaborative filtering
Introduction to RBMs
By their textbook definition, RBMs are probabilistic graphical models, which—given what we've already covered regarding the structure of neural networks—simply...