Practice Evaluation of Recommender Systems - 11.6 | 11. Recommender Systems | Data Science Advance
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11.6 - Evaluation of Recommender Systems

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the purpose of offline evaluation?

💡 Hint: Think about how past data can help inform us.

Question 2

Easy

Define precision in the context of recommender systems.

💡 Hint: Consider the accuracy of the recommendations.

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 does precision measure?

  • The total number of recommendations
  • The ratio of correct recommendations to total recommendations
  • The time users spend on recommendations

💡 Hint: Think about how often the recommendations are correct.

Question 2

True or False: Recall is concerned with how many relevant items were actually shown to users.

  • True
  • False

💡 Hint: Consider what it means by 'relevant' items.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Create a case where offline evaluation indicates a very high precision, but the online evaluation shows a very low click-through rate. Discuss possible reasons.

💡 Hint: Reflect on how user preferences can change over time.

Question 2

Using a hypothetical dataset, calculate RMSE given the following actual and predicted ratings: Actual: [5, 4, 3, 2], Predicted: [5, 3, 4, 1]. What does this RMSE say about the model’s accuracy?

💡 Hint: Consider the importance of smaller RMSE values.

Challenge and get performance evaluation