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.
Este exercício faz parte do curso
Building Recommendation Engines in Python
Instruções do exercício
- Find the highest ranked movies for
User_5by sorting all the reviews generated forUser_5from high to low.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Sort the ratings of User 5 from high to low
user_5_ratings = ____.____[____,:].____(____=____)
print(user_5_ratings)