Practice Recommender Systems (11) - Recommender Systems

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11 questions

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5 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is a recommender system?
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Think about how platforms like Netflix suggest shows.
Question 2.
What does cold start mean in recommender systems?
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Consider why it might be hard to recommend something to someone new.
3Question 3.
What is a recommender system?
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Think about a situation where suggestions are made to you.
4Question 4.
Is collaborative filtering reliant on item features?
- True
- False
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Recall how it identifies user preferences.
Question 5.
Design a recommender system for a new food delivery app. How would you address both cold start and sparsity issues?
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Consider how initial user input could guide future recommendations.
Question 6.
Develop an algorithm that utilizes matrix factorization for a recommender system. Explain how your algorithm will improve recommendations.
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Think about how you can translate user-item interactions into latent features.

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