Practice Item-based Collaborative Filtering - 11.2.2.b | 11. Recommender Systems | Data Science Advance
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11.2.2.b - Item-based Collaborative Filtering

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is item-based collaborative filtering?

💡 Hint: Think about what it means to recommend based on items rather than users.

Question 2

Easy

Give an example of an application that uses item-based collaborative filtering.

💡 Hint: Consider how you see suggestions while shopping online.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does item-based collaborative filtering focus on?

  • User similarity
  • Item similarity
  • User preferences

💡 Hint: Remember the method's name: it involves items.

Question 2

True or False: Item-based collaborative filtering is less stable than user-based filtering.

  • True
  • False

💡 Hint: Think about which type of data is more variable.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given two potential items, A and B, describe how you would determine their similarity for recommendations using item-based collaborative filtering.

💡 Hint: Consider the tools or metrics that assess multi-user engagement with both items.

Question 2

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?

💡 Hint: Think about how integrating different data sources can produce better outcomes.

Challenge and get performance evaluation