Summary  
This chapter explains how to transform raw user–item interaction data into the numeric, indexed format required by Spark’s ALS implementation—covering data cleaning, mapping IDs to indices, creating Rating objects, splitting into training and test sets, and caching for performance.  

General domain of usage  
Recommendation 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.

Preparing Data for ALS in Spark