Chapter 9. Recommendation Systems Using Factorization Machines
Factorization models are very popular in recommendation systems because they can be used to discover latent features underlying the interactions between two different kinds of entities. In this chapter, we will provide several examples of how to develop recommendation system for predictive analytics.
We will see the theoretical background of recommendation systems, such as matrix factorization. Later in the chapter, we will see how to use a collaborative approach to develop a movie recommendation system. Finally, will see how to use Factorization Machines (FMs) and improved versions of them to develop more robust recommendation systems.
In summary, the following topics will be covered in this chapter:
- Recommendation systems
- A movie recommendation system using the collaborative filtering approach
- K-means for clustering similar movies
- FM-based recommendation systems
- Using improved FMs for movie recommendation
