Practice Matrix Factorization (11.4.2) - Recommender Systems - Data Science Advance

Practice: Matrix Factorization

Lesson 11.4.2Recommender Systems

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10 questions

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6 sample questions

Question 1.
What is matrix factorization?
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Think about how movie recommendations work.
Question 2.
Name one technique used in matrix factorization.
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Consider different mathematical methods for matrix analysis.
3Question 3.
What is the primary purpose of matrix factorization?
- To enhance data privacy
- To decompose matrices into latent factors
- To store user preferences
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Think about what insights can be derived from matrix decomposition.
4Question 4.
True or False: NMF allows negative values in its matrices.
- True
- False
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Consider the implications of negative ratings.
Question 5.
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.
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Break the steps into individual matrix computations.
Question 6.
Describe a scenario where the choice between SVD and NMF might fundamentally impact the results of a recommendation engine. Provide rationale for each choice.
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Think about the user base and nature of data you would deal with.

6 more questions in this section. Enroll free to practise them all.