User profile based recommendations
Now that you have built the user profile based on the aggregate of the individual movies they enjoyed, you can compare it to the larger tfidf_summary_df DataFrame that you have been working with to generate suggestions. As you would not want to suggest movies that the user has already watched, you will first find a subset of the tfidf_summary_df DataFrame that does not contain any of the previously watched movies.
The DataFrame user_prof that you generated in the last exercise that contains a single column representing the user has been loaded for you. Similarly, the list_of_movies_enjoyed has been loaded so you can exclude them from the predictions.
This exercise is part of the course
Building Recommendation Engines in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
from sklearn.metrics.pairwise import cosine_similarity
# Find subset of tfidf_df that does not include movies in list_of_movies_enjoyed
tfidf_subset_df = tfidf_df.____(list_of_movies_enjoyed, axis=____)