Practice Core Algorithms (11.4) - Recommender Systems

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10 questions

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6 sample questions

Question 1.
What does KNN stand for?
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Remember the word 'Neighbors.'
Question 2.
What is the purpose of Matrix Factorization?
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Think about uncovering secrets in data!
3Question 3.
What does K in KNN represent?
- Number of dimensions
- Number of neighbors
- Number of items
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Think about the meaning of 'neighbors' in a community.
4Question 4.
True or False: Matrix Factorization can only be applied to explicit feedback data.
- True
- False
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Consider whether you always need ratings.
Question 5.
Using KNN, how would you recommend new items to users without historical data?
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Consider demographic data.
Question 6.
Design a hybrid recommender system combining Matrix Factorization with KNN. What steps would you take?
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Think about leveraging strengths of both methods.

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