ALS is an optimization technique to solve Matrix Factorization problems; this technique is powerful, achieves good performance, and has proven to be relatively easy to implement in a parallel fashion. Hence, it is well suited for platforms such as Spark. At the time of writing this book, it is the only recommendation model implemented in Spark ML.
ALS works by iteratively solving a series of least squares regression problems. In each iteration, one of the user- or item-factor matrices is treated as fixed, while the other one is updated using the fixed factor and the rating data. Then, the factor matrix that was solved for is, in turn, treated as fixed, while the other one is updated. This process continues until ...
