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  3. Feature Engineering for NLP in Python

Exercise

Plot recommendation engine

In this exercise, we will build a recommendation engine that suggests movies based on similarity of plot lines. You have been given a get_recommendations() function that takes in the title of a movie, a similarity matrix and an indices series as its arguments and outputs a list of most similar movies. indices has already been provided to you.

You have also been given a movie_plots Series that contains the plot lines of several movies. Your task is to generate a cosine similarity matrix for the tf-idf vectors of these plots.

Consequently, we will check the potency of our engine by generating recommendations for one of my favorite movies, The Dark Knight Rises.

Instructions

100 XP
  • Initialize a TfidfVectorizer with English stop_words. Name it tfidf.
  • Construct tfidf_matrix by fitting and transforming the movie plot data using fit_transform().
  • Generate the cosine similarity matrix cosine_sim using tfidf_matrix. Don't use cosine_similarity()!
  • Use get_recommendations() to generate recommendations for 'The Dark Knight Rises'.