Practice Building a Simple Recommender in Python (Collaborative Filtering) - 11.7 | 11. Recommender Systems | Data Science Advance
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11.7 - Building a Simple Recommender in Python (Collaborative Filtering)

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does SVD stand for?

💡 Hint: Think about matrix decomposition and factorization in linear algebra.

Question 2

Easy

What library do we use to build recommender systems in Python?

💡 Hint: It has 'surprise' in its name.

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 percentage of the dataset is typically used for training?

  • 50%
  • 70%
  • 80%
  • 90%

💡 Hint: Think about common practices in data science.

Question 2

True or False: RMSE can be used to measure the accuracy of predictions.

  • True
  • False

💡 Hint: Consider the definition of RMSE.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a movie dataset. Propose how you would structure your recommendation system using collaborative filtering.

💡 Hint: Think about data attributes, algorithm choices, and user patterns.

Question 2

Create a Python function that takes the user-item matrix, applies SVD, and returns the predicted ratings.

💡 Hint: Focus on the dataset initialization and model fitting process.

Challenge and get performance evaluation