In this section, let's learn about the different methods present in the MLlib recommendation engine module. The current recommendation engine module helps us build the model-based collaborative filtering approach using the Alternating Least Squares matrix factorization model to generate recommendations.
The main methods available for building collaborative filtering are as follows:
ALS()constructor is invoked and its instance is created with all the required parameters, such as user column name, item column name, rating column name, rank, regularization parameter (regParam), maximum iterations (maxIter), and so on supplied.
fit()method is used to generate the model. This method takes the ...