PySpark and AWS: Master Big Data with PySpark and AWS - Utility Matrix

The video tutorial explains utility metrics and their role in recommender systems. It describes how user ratings for movies are used to fill a utility matrix, predicting ratings for unwatched movies. The tutorial highlights the application of these systems in platforms like Netflix, ecommerce, and social media, where they recommend products or content based on user feedback and ratings. The process involves analyzing user data, predicting ratings, and suggesting items with the highest predicted ratings. The video concludes with a summary of how recommender systems function.

What is the significance of maximum ratings in the context of recommender systems?

In what ways do e-commerce websites utilize utility metrics according to the text?