Recommender Systems with Machine Learning - Making Recommendations

The video tutorial explains how to create a content-based movie recommender function. It covers defining the function, taking inputs, calculating closest titles and distance scores, and filtering and sorting similar movies. The tutorial concludes with running the function using an example and previews the next video on collaborative and item-based filtering.

What is the primary purpose of the content-based recommender function?

• To sort movies alphabetically

• To recommend movies based on user preferences

• To calculate movie ratings

• To list all available movies

What is the role of the 'find_closest_title' function in the recommender?

• To sort movies by release date

• To identify the closest matching movie title

• To calculate the distance score

• To find the most popular movie

How is the movie index obtained in the recommender function?

• By using the 'get_index_from_title' function

• By calculating the average rating

• By sorting the movies alphabetically

• By using the movie's release year

What is the purpose of the similarity matrix in the recommender?

• To calculate the similarity between movies

• To store movie ratings

• To display movie titles

• To list all movie genres

How are similar movies filtered in the recommender function?

• By selecting movies with the highest ratings

• By filtering based on the movie index

• By sorting the movie list alphabetically

• By using a random selection

What is the final output of the content-based recommender function?

• The highest-rated movie

• A list of similar movies to the input movie

• A list of all movies

• The oldest movie in the dataset

What is the basis for recommending movies in this content-based system?

• The movie's director

• The movie's runtime

• The movie's genre

• The movie's box office earnings