Summary
This chapter explains the alternating least squares matrix factorization algorithm, showing how to implement code to iteratively solve for latent feature matrices with regularization and evaluate model performance.

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.

How ALS Works