Practice User-based Collaborative Filtering (11.2.2.a) - Recommender Systems

Practice: User-based Collaborative Filtering

Lesson 11.2.2.aRecommender Systems

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is user-based collaborative filtering?
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Think about how friends might recommend movies to each other.
Question 2.
What is the cold start problem?
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Consider what happens when someone joins a new platform.
3Question 3.
What does user-based collaborative filtering rely on?
- Item features
- User preferences of similar users
- User demographics
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Think about whose opinions are most influential in recommendations.
4Question 4.
True or False: User-based collaborative filtering requires that the items have detailed feature data.
- True
- False
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Consider if features play a role in recommendations.
Question 5.
Propose a mixed strategy to handle the cold start problem for a new social media platform aiming to recommend posts effectively.
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Consider how existing platforms onboard new users.
Question 6.
Analyze the impact of sparse data on user-based collaborative filtering performance. Discuss potential solutions.
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Think creatively about overcoming data gaps.

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