Summary  
The chapter covers how to implement evaluation for implicit-feedback alternating least squares models by generating top-K item predictions and calculating ranking metrics such as precision@K, recall@K, AUC, and NDCG.  

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 Implicit ALS Models