Practice Nearest Neighbor Models (11.4.1) - Recommender Systems

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6 sample questions

Question 1.
What is the main function of Nearest Neighbor Models?
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Think about how recommendations work.
Question 2.
Name one similarity metric commonly used in Nearest Neighbor Models.
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What do we use to measure two things being alike?
3Question 3.
What does KNN stand for?
- K-Nearest Neighbors
- K-Normal Neighbors
- K-Next Neighbors
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What does the 'K' typically refer to in metrics?
4Question 4.
True or False: Item-based collaborative filtering relies on similarities between users.
- True
- False
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Think about which group is being compared.
Question 5.
How would you address the cold start problem in a new recommender system using Nearest Neighbor Models?
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Consider how you can utilize user data before they engage with the system.
Question 6.
Explain how changes in K (the number of neighbors considered) could affect the quality of recommendations.
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Think about how varying K influences the local neighborhood around each user.

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