Recommender Systems Complete Course Beginner to Advanced - Basics of Recommender System: User Rating Matrix

The video tutorial discusses different methods to estimate 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 direct user feedback. The organization of rating scales, including the choice between even and odd scales, affects user comfort and the number of ratings received. The tutorial also covers the concept of user rating matrix density and how biases in user ratings can impact recommendation systems.

What are implicit ratings based on?

• User's explicit feedback

• Surveys conducted by the system

• User's behavior like viewing time

• Random selection by the system

What is a key consideration when organizing a rating scale?

• The range and granularity of the scale

• The number of items rated

• The time of day ratings are given

• The color of the scale

What is a potential downside of using a large rating scale?

• It requires less effort from users

• It is easier for users to choose

• It may result in fewer ratings

• It provides less detailed feedback

What is a characteristic of an even rating scale?

• It forces users to choose a side

• It includes a neutral option

• It results in more ratings

• It is always preferred by users

Why might users prefer an odd rating scale?

• It allows for neutral ratings

• It is faster to use

• It is more colorful

• It is less confusing

What does a low URM density indicate?

• A high number of ratings

• A low number of ratings

• A balanced rating distribution

• A high level of user engagement

What bias is created by users preferring to give neutral ratings?

• A bias towards negative experiences

• No bias is created

• A bias towards positive experiences

• A bias towards neutral experiences