Practice Recommender Systems - 11 | 11. Recommender Systems | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a recommender system?

πŸ’‘ Hint: Think about how platforms like Netflix suggest shows.

Question 2

Easy

What does cold start mean in recommender systems?

πŸ’‘ Hint: Consider why it might be hard to recommend something to someone new.

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 is a recommender system?

πŸ’‘ Hint: Think about a situation where suggestions are made to you.

Question 2

Is collaborative filtering reliant on item features?

  • True
  • False

πŸ’‘ Hint: Recall how it identifies user preferences.

Solve 3 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a recommender system for a new food delivery app. How would you address both cold start and sparsity issues?

πŸ’‘ Hint: Consider how initial user input could guide future recommendations.

Question 2

Develop an algorithm that utilizes matrix factorization for a recommender system. Explain how your algorithm will improve recommendations.

πŸ’‘ Hint: Think about how you can translate user-item interactions into latent features.

Challenge and get performance evaluation