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.
Questo esercizio fa parte del corso
Building Recommendation Engines in Python
Istruzioni dell'esercizio
- Find the highest ranked movies for
User_5by sorting all the reviews generated forUser_5from high to low.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Sort the ratings of User 5 from high to low
user_5_ratings = ____.____[____,:].____(____=____)
print(user_5_ratings)