Spark MLlib

MLlib ALS algorithm takes the training data of the RDD type, that is, Distributed Datasets [Rating] and trains a model, which is a MatrixFactorizationModel object.

The technique we are using here is known as . Let's assume User A likes Product A, Product B, and Product C and rated them with a score. Then, let's assume User B likes Product B, Product C, and Product D and gave a similar rating to the score User A gave for Product B and Product C. Now, using Collaborative Filtering, one can find out what User A would rate for Product D or what User B would rate for Product A as we have some commonality between User A and User B--they both rated Product B and Product C similarly.