Summary
This chapter explains how to implement code to train a collaborative filtering model using the Alternating Least Squares algorithm, including data splitting, hyperparameter configuration, and iterative optimization of latent factors. It also covers evaluating model performance with quantitative metrics.

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.

Training an ALS Model