Practice Reinforcement Learning (11.9.2) - Recommender Systems

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8 questions

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Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is Reinforcement Learning?
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Think of how learning happens based on success or failure.
Question 2.
How does user feedback influence recommendations in RL?
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Recall the Action-Reward concept.
3Question 3.
What does Reinforcement Learning focus on?
- Data analysis
- Learning from actions
- User profiling
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Think about actions leading to rewards.
4Question 4.
True or False: Feedback from users in RL is irrelevant to future recommendations.
- True
- False
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Consider how feedback influences learning.
Question 5.
Design a simple RL algorithm for a movie recommender system. Discuss the states and actions involved.
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Consider user feedback as a way to score the effectiveness of suggestions.
Question 6.
Evaluate the potential drawbacks of using Reinforcement Learning in highly transactional domains.
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Focus on user behavior variability over time.

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