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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a recommender system?
💡 Hint: Think about how platforms like Netflix suggest shows.
Question 2
Easy
What does cold start mean in recommender systems?
💡 Hint: Consider why it might be hard to recommend something to someone new.
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 a recommender system?
💡 Hint: Think about a situation where suggestions are made to you.
Question 2
Is collaborative filtering reliant on item features?
- True
- False
💡 Hint: Recall how it identifies user preferences.
Solve 3 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a recommender system for a new food delivery app. How would you address both cold start and sparsity issues?
💡 Hint: Consider how initial user input could guide future recommendations.
Question 2
Develop an algorithm that utilizes matrix factorization for a recommender system. Explain how your algorithm will improve recommendations.
💡 Hint: Think about how you can translate user-item interactions into latent features.
Challenge and get performance evaluation