Practice Matrix Factorization - 11.4.2 | 11. Recommender Systems | Data Science Advance
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Matrix Factorization

11.4.2 - Matrix Factorization

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Practice Questions

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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.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

Challenge 2 Hard

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.

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