Chapter 11. Finding hidden genres with matrix factorization
The matrix is only numbers, and this chapter is about the matrix and how to create one:
- 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 ...
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