Instead of explicitly defining similarity metrics, we can utilize deep learning techniques in order to learn good representations and mappings of the feature space. There are a number of ways to employ neural networks in order to build recommendation systems. In this chapter, we will present two of the simplest ways to do so in order to demonstrate the ability to incorporate ensemble learning into the system. The most important piece that we will utilize in our networks is the embedding layer. These layer types accept an integer index as input and map it to an n-dimensional space. For example, a two-dimensional mapping could map 1 to [0.5, 0.5]. Utilizing these layers, we will be able to feed the user's index and the movie's index to our network, and the network will predict the rating for the specific user-movie combination.
The first architecture...
