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
Exercise instructions
- Find the highest ranked movies for
User_5
by sorting all the reviews generated forUser_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)