Practice Online Recommendations and Ads - 9.11.5 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.11.5 - Online Recommendations and Ads

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What do we mean by exploration in online recommendations?

πŸ’‘ Hint: Think about variety vs. familiarity.

Question 2

Easy

What is a Multi-Armed Bandit?

πŸ’‘ Hint: Related to a gambling machine.

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 the primary goal of reinforcement learning in online recommendations?

  • Maximize User Engagement
  • Minimize Costs
  • Increase Data Collection

πŸ’‘ Hint: Consider what drives platform success.

Question 2

True or False: Contextual bandits always provide the same recommendations regardless of user profile.

  • True
  • False

πŸ’‘ Hint: Think about how personalization works.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a user interface feature that uses a multi-armed bandit approach to improve content recommendations on a blog.

πŸ’‘ Hint: Think about how you could gather feedback on user preferences.

Question 2

Simulate a simple scenario comparing the effectiveness of exploration vs. exploitation in an online advertisement campaign.

πŸ’‘ Hint: What metrics would you choose to measure efficacy?

Challenge and get performance evaluation