Summary  
This chapter explains how to configure and tune the Alternating Least Squares (ALS) algorithm’s parameters—such as rank, regularization weight, and confidence scaling—for training on implicit feedback datasets.

General domain of usage  
Recommender systems

Master the fundamentals and advanced techniques of building recommendation engines using PySpark, focusing on real-world datasets from the movie and music domains. Learn collaborative and content-based filtering, explicit and implicit feedback, matrix factorization, and practical model evaluation.

Explore the theory and practical implementation of recommendation engines using PySpark, focusing on movie and music datasets.

Configuring ALS for Implicit Data