Making recommendations with SVD

Now that you have the recalculated matrix with all of its gaps filled in, the next step is to use it to generate predictions and recommendations.

Using calc_pred_ratings_df that you generated in the last exercise, with all rows and columns filled, find the movies that User_5 is most likely to enjoy.

Это упражнение является частью курса

Building Recommendation Engines in Python

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Инструкции к упражнению

Find the highest ranked movies for User_5 by sorting all the reviews generated for User_5 from high to low.

Интерактивное практическое упражнение

Попробуйте выполнить это упражнение, дополнив этот пример кода.

# Sort the ratings of User 5 from high to low
user_5_ratings = ____.____[____,:].____(____=____)

print(user_5_ratings)

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