The recommender function
In this exercise, we will build a recommender function get_recommendations(), as discussed in the lesson and the previous exercise. As we know, it takes in a title, a cosine similarity matrix, and a movie title and index mapping as arguments and outputs a list of 10 titles most similar to the original title (excluding the title itself).
You have been given a dataset metadata that consists of the movie titles and overviews. The head of this dataset has been printed to console.
Cet exercice fait partie du cours
Feature Engineering for NLP in Python
Instructions
- Get index of the movie that matches the title by using the
titlekey ofindices. - Extract the ten most similar movies from
sim_scoresand store it back insim_scores.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Generate mapping between titles and index
indices = pd.Series(metadata.index, index=metadata['title']).drop_duplicates()
def get_recommendations(title, cosine_sim, indices):
# Get index of movie that matches title
idx = ____[____]
# Sort the movies based on the similarity scores
sim_scores = list(enumerate(cosine_sim[idx]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
# Get the scores for 10 most similar movies
sim_scores = sim_scores[____]
# Get the movie indices
movie_indices = [i[0] for i in sim_scores]
# Return the top 10 most similar movies
return metadata['title'].iloc[movie_indices]