Practice Content-based Filtering (11.2.1) - Recommender Systems - Data Science Advance

Practice: Content-Based Filtering

Lesson 11.2.1Recommender Systems

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9 questions

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4 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.
Define content-based filtering in your own words.
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Think about how it personalizes suggestions.
Question 2.
What is an item feature? Give an example.
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Focus on what describes the item.
3Question 3.
What does content-based filtering primarily rely on?
- User preferences
- Item features
- Similar users
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Think about what aspects of the items are being used for recommendations.
4Question 4.
True or False: Content-based filtering can recommend a radically different item that a user has never interacted with.
- True
- False
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Remember the focus of content-based filtering.
Question 5.
You have a user who loves horror films but suddenly starts liking romantic comedies. How might this affect the user's profile and the recommendations provided?
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Think about how databases track changes in user preferences.
Question 6.
Discuss how content-based filtering could be applied to a music streaming service, detailing features that would be important.
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Consider different aspects of music that could relate to user preferences.

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