Collaborative Filtering Concepts and Challenges

What is the primary goal of building a movie recommender system in this course?

• To analyze the box office success of various films.

• To categorize movies based on their genres.

• To find a movie that both Jabril and John-Green-bot will enjoy.

• To create a list of the most popular movies worldwide.

Why is it important to handle missing data in the MovieLens dataset?

• To avoid storing unnecessary zeros and save space.

• To ensure all movies have the same number of ratings.

• To increase the dataset size for better analysis.

• To make sure every movie is rated by at least one person.

What problem arises when the recommender system doesn't know anything about the users?

• The system cannot make personalized recommendations.

• The system fails to recommend any movies at all.

• The system recommends only the most popular movies.

• The system recommends movies based on random selection.

In user-user collaborative filtering, what does each item represent?

• A rating scale for movies.

• A genre of movies.

• A cluster of similar users.

• A single dimension in a multi-dimensional space.

What is the effect of setting a small neighborhood size in user-user collaborative filtering?

• It considers fewer people with more similar tastes.

• It results in a larger dataset for analysis.

• It leads to more generic recommendations.

• It increases the diversity of movie recommendations.

What is a potential downside of using a large neighborhood size in collaborative filtering?

• It increases the computational complexity significantly.

• It may lead to recommendations that are too specific.

• It limits the number of movies that can be recommended.

• It can result in recommendations that are too generic.

How does the system handle movies that only one user has rated when creating a combined dataset?

• It ignores those movies entirely.

• It duplicates the rating for the other user.

• It adds the single rating to the combined list.

• It averages the rating with a default value.

What is the purpose of creating a Jabril-Green-bot hybrid dataset?

• To create a single list of movies both users might enjoy.

• To simplify the process of rating movies.

• To eliminate the need for user-user collaborative filtering.

• To increase the number of movies in the dataset.

What is a benefit of using a larger dataset from MovieLens?

• It allows for faster computation of recommendations.

• It includes a wider variety of movies for analysis.

• It simplifies the process of collaborative filtering.

• It reduces the complexity of the recommendation algorithm.

How can the recommender system be adapted for a group of users?

• By averaging the ratings of all group members.

• By selecting the highest rating from the group.

• By using only the ratings of the group leader.

• By ignoring individual preferences and focusing on popular movies.