Summary  
This chapter shows how to implement code to compute the root-mean-square error by comparing predicted versus actual values from an ALS model to evaluate its predictive accuracy.

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.

Evaluating ALS with RMSE