Machine Learning with Spark - Second Edition

Matrix factorization

Since Spark's recommendation models currently only include an implementation of Matrix factorization, we will focus our attention on this class of models. This focus is with good reason; however, these types of models have consistently been shown to perform extremely well in collaborative filtering and were among the best models in well-known competitions, such as the Netflix prize.

Matrix Factorization assumes that:

• Each user can be described by n attributes or features. For example, feature one might be a number that says how much each user likes action movies.
• Each item can be described by a set of n attributes or features. To connect with the preceding example, feature one for the movie might be a number that says ...