O'Reilly logo

Building Recommendation Engines by Suresh Kumar Gorakala

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

MLlib recommendation engine module

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(): The 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(): The fit() method is used to generate the model. This method takes the ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required