Recommender Systems with Machine Learning - Active Users and Popular Movies

The video tutorial explains how to count movies using data frames, calculate quantiles, filter movies based on popularity, manipulate data frames to drop unpopular movies, analyze user ratings, and set up collaborative filtering. It covers creating data frames, using group by functions, and applying thresholds to filter data. The tutorial also discusses calculating active users and setting up collaborative filtering for recommendation systems.

What is the purpose of grouping ratings by movie ID in the DataFrame?

• To sort movies by their release date

• To count the number of ratings each movie received

• To calculate the average rating of each movie

• To list all unique movie IDs

How is the popularity threshold used in filtering movies?

• It determines the minimum rating a movie must have

• It sets the maximum number of ratings a movie can receive

• It identifies movies with a count above a certain number

• It filters movies based on their release year

What is the result of dropping unpopular movies from the DataFrame?

• The DataFrame is filtered by genre

• The DataFrame includes only movies with a high number of ratings

• The DataFrame is sorted by movie title

• The DataFrame contains only movies with high ratings

What is the significance of counting ratings per user?

• To find users who rate movies the highest

• To determine the most active users

• To identify users who rate movies the lowest

• To calculate the average rating given by each user

How are active users identified in the DataFrame?

• By the genres they prefer

• By their average rating

• By the movies they have rated

• By the number of ratings they have given

What is the next step after identifying active users and popular movies?

• Creating a new movie recommendation algorithm

• Sorting movies by popularity

• Analyzing user demographics

• Developing a collaborative filtering system

What data is used to make collaborative filtering recommendations?

• Both active users and popular movies

• Only the most popular movies

• Only the most active users

• All available movie data