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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does collaborative filtering mean?
π‘ Hint: Think of how friends influence each other's choices.
Question 2
Easy
What is a user-item interaction matrix?
π‘ Hint: Itβs like a scorecard for user preferences.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary assumption of collaborative filtering?
π‘ Hint: Think about why friends recommend movies to each other.
Question 2
Is the cold start problem a challenge for collaborative filtering?
π‘ Hint: Consider what happens when someone is new to a system.
Solve 2 more questions and get performance evaluation
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.
π‘ 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.
π‘ Hint: Consider what happens to calculations as the dataset increases in size.
Challenge and get performance evaluation