Practice Online Recommendations And Ads (9.11.5) - Reinforcement Learning and Bandits
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Online Recommendations and Ads

Practice - Online Recommendations and Ads

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What do we mean by exploration in online recommendations?

💡 Hint: Think about variety vs. familiarity.

Question 2 Easy

What is a Multi-Armed Bandit?

💡 Hint: Related to a gambling machine.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary goal of reinforcement learning in online recommendations?

Maximize User Engagement
Minimize Costs
Increase Data Collection

💡 Hint: Consider what drives platform success.

Question 2

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

True
False

💡 Hint: Think about how personalization works.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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

💡 Hint: Think about how you could gather feedback on user preferences.

Challenge 2 Hard

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

💡 Hint: What metrics would you choose to measure efficacy?

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Reference links

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