Summary  
This chapter explains how to infer quantitative user ratings from implicit behavior data by applying collaborative filtering and matrix factorization techniques.

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.

Inferring Ratings from User Behavior