Practice Collaborative Filtering Recommender Systems (13.4.2) - Advanced ML Topics & Ethical Considerations (Weeks 13)
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Collaborative Filtering Recommender Systems

Practice - Collaborative Filtering Recommender Systems

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does collaborative filtering mean?

💡 Hint: Think of how friends influence each other's choices.

Question 2 Easy

What is a user-item interaction matrix?

💡 Hint: It’s like a scorecard for user preferences.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary assumption of collaborative filtering?

It relies solely on item attributes.
It assumes similarity in user preferences leads to similar choices.
It disregards past user experiences.

💡 Hint: Think about why friends recommend movies to each other.

Question 2

Is the cold start problem a challenge for collaborative filtering?

True
False

💡 Hint: Consider what happens when someone is new to a system.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a simple collaborative filtering model for an online bookstore. Define how the user-item interaction matrix would be structured and suggest potential methods to tackle the cold start problem.

💡 Hint: Think about how you would suggest a book to a new user who hasn't rated anything yet.

Challenge 2 Hard

Critically analyze the scalability issues of user-based collaborative filtering with a large dataset. Suggest ways to improve the performance.

💡 Hint: Consider what happens to calculations as the dataset increases in size.

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