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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is matrix factorization?
💡 Hint: Think about how movie recommendations work.
Question 2
Easy
Name one technique used in matrix factorization.
💡 Hint: Consider different mathematical methods for matrix analysis.
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 purpose of matrix factorization?
- To enhance data privacy
- To decompose matrices into latent factors
- To store user preferences
💡 Hint: Think about what insights can be derived from matrix decomposition.
Question 2
True or False: NMF allows negative values in its matrices.
- True
- False
💡 Hint: Consider the implications of negative ratings.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a user-item interaction matrix of ratings from 1-5 for 5 users and 5 items, demonstrate how you would apply SVD to find the latent factors. Include detailed computation steps.
💡 Hint: Break the steps into individual matrix computations.
Question 2
Describe a scenario where the choice between SVD and NMF might fundamentally impact the results of a recommendation engine. Provide rationale for each choice.
💡 Hint: Think about the user base and nature of data you would deal with.
Challenge and get performance evaluation