Practice Item-based Collaborative Filtering (11.2.2.b) - Recommender Systems

Practice: Item-based Collaborative Filtering

Lesson 11.2.2.bRecommender Systems

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8 questions

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2 cards from this section. Quick, good the night before a test.
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6 sample questions

Question 1.
What is item-based collaborative filtering?
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Think about what it means to recommend based on items rather than users.
Question 2.
Give an example of an application that uses item-based collaborative filtering.
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Consider how you see suggestions while shopping online.
3Question 3.
What does item-based collaborative filtering focus on?
- User similarity
- Item similarity
- User preferences
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Remember the method's name: it involves items.
4Question 4.
True or False: Item-based collaborative filtering is less stable than user-based filtering.
- True
- False
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Think about which type of data is more variable.
Question 5.
Given two potential items, A and B, describe how you would determine their similarity for recommendations using item-based collaborative filtering.
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Consider the tools or metrics that assess multi-user engagement with both items.
Question 6.
Evaluate the effectiveness of item-based collaborative filtering for a new video streaming platform just launched. What would be your approach to recommend content without existing user data?
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Think about how integrating different data sources can produce better outcomes.

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