Practice Sparsity (11.5.2) - Recommender Systems - Data Science Advance

Practice: Sparsity

Lesson 11.5.2Recommender Systems

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10 questions

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6 sample questions

Question 1.
Define sparsity in the context of recommender systems.
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Think about the number of blanks in a user-item matrix.
Question 2.
What is matrix factorization?
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Consider how you might simplify complex data by breaking it down.
3Question 3.
What does sparsity refer to in recommender systems?
- Abundance of data
- Lack of user interactions
- Well-structured matrix
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Consider how many users engage with items.
4Question 4.
True or False: Matrix factorization can help uncover patterns in sparse data.
- True
- False
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Reflect on how breaking down data helps us see connections.
Question 5.
Consider a user-item matrix with 100 rows (users) and 50 columns (items). If there's an average of only 2 interactions per user, discuss the implications of sparsity.
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Reflect on how the limited interactions impact decision-making.
Question 6.
Design a hybrid recommender system that addresses the sparsity problem using matrix factorization and collaborative filtering. Describe how these techniques would work together.
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Consider how each technique can fill in the gaps left by the other.

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