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.

This exercise is part of the course

Feature Engineering for NLP in Python

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Exercise instructions

Get index of the movie that matches the title by using the title key of indices.
Extract the ten most similar movies from sim_scores and store it back in sim_scores.

Hands-on interactive exercise

Have a go at this exercise by completing this sample 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]

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