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.

Diese Übung ist Teil des Kurses

<Kurs>Feature Engineering for NLP in Python</Kurs>

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Übungsanweisungen

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.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# 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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