Practice Building A Simple Recommender In Python (collaborative Filtering) (11.7)

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9 questions

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

Question 1.
What does SVD stand for?
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Think about matrix decomposition and factorization in linear algebra.
Question 2.
What library do we use to build recommender systems in Python?
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It has 'surprise' in its name.
3Question 3.
What percentage of the dataset is typically used for training?
- 50%
- 70%
- 80%
- 90%
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Think about common practices in data science.
4Question 4.
True or False: RMSE can be used to measure the accuracy of predictions.
- True
- False
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Consider the definition of RMSE.
Question 5.
You have a movie dataset. Propose how you would structure your recommendation system using collaborative filtering.
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Think about data attributes, algorithm choices, and user patterns.
Question 6.
Create a Python function that takes the user-item matrix, applies SVD, and returns the predicted ratings.
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Focus on the dataset initialization and model fitting process.

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