Practice Collaborative Filtering Recommender Systems - 13.4.2 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 13) | Machine Learning
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13.4.2 - Collaborative Filtering Recommender Systems

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does collaborative filtering mean?

πŸ’‘ Hint: Think of how friends influence each other's choices.

Question 2

Easy

What is a user-item interaction matrix?

πŸ’‘ Hint: It’s like a scorecard for user preferences.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary assumption of collaborative filtering?

  • It relies solely on item attributes.
  • It assumes similarity in user preferences leads to similar choices.
  • It disregards past user experiences.

πŸ’‘ Hint: Think about why friends recommend movies to each other.

Question 2

Is the cold start problem a challenge for collaborative filtering?

  • True
  • False

πŸ’‘ Hint: Consider what happens when someone is new to a system.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a simple collaborative filtering model for an online bookstore. Define how the user-item interaction matrix would be structured and suggest potential methods to tackle the cold start problem.

πŸ’‘ Hint: Think about how you would suggest a book to a new user who hasn't rated anything yet.

Question 2

Critically analyze the scalability issues of user-based collaborative filtering with a large dataset. Suggest ways to improve the performance.

πŸ’‘ Hint: Consider what happens to calculations as the dataset increases in size.

Challenge and get performance evaluation