- You’ll learn about dimensionality reduction recommender algorithms.
- Reducing similarity will help you find latent (hidden) factors in the data.
- You’ll train and use a singular value decomposition (SVD) to create recommendations.
- You’ll learn how to fold in new users and items into an SVD.
- You’ll look at another matrix factorization model called the Funk SVD, which is more flexible than the original SVD.
What have you learned so far? In chapter 8, we looked at collaborative filtering using neighbor-based filtering. In this chapter, we’re going to return to collaborative filtering, but this time we’re not talking about neighborhoods. Instead, we’ll explore latent factors. In chapter 10, we talked about latent factors, but at that point, we talked about latent factors in the content data. Now we’ll look at latent factors in relation to collaborative filtering, which means in behavioral data.