Recommender Systems with Machine Learning - User Rating Matrix

The video tutorial discusses different methods of estimating user opinions on items, focusing on implicit and explicit ratings. Implicit ratings are derived from user behavior, such as viewing time or purchase history, while explicit ratings involve users grading items directly. The tutorial explores the organization of rating scales, including the choice between large and small scales, and even versus odd scales. It highlights the impact of neutral ratings on system density and the bias introduced by users' tendency to rate only positive experiences. The concept of user rating matrix density is also explained.

In what ways can the organization of the rating scale influence user engagement?

Explain the concept of user rating matrix and its importance in recommendation systems.

What bias do users exhibit when providing ratings, and how does it affect rating distribution?