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.

This exercise is part of the course

Building Recommendation Engines in Python

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Exercise instructions

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

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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

print(user_5_ratings)

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