TED talk recommender

In this exercise, we will build a recommendation system that suggests TED Talks based on their transcripts. You have been given a get_recommendations() function that takes in the title of a talk, a similarity matrix and an indices series as its arguments, and outputs a list of most similar talks. indices has already been provided to you.

You have also been given a transcripts series that contains the transcripts of around 500 TED talks. Your task is to generate a cosine similarity matrix for the tf-idf vectors of the talk transcripts.

Consequently, we will generate recommendations for a talk titled '5 ways to kill your dreams' by Brazilian entrepreneur Bel Pesce.

This exercise is part of the course

Feature Engineering for NLP in Python

View Course

Exercise instructions

Initialize a TfidfVectorizer with English stopwords. Name it tfidf.
Construct tfidf_matrix by fitting and transforming transcripts.
Generate the cosine similarity matrix cosine_sim using tfidf_matrix.
Use get_recommendations() to generate recommendations for '5 ways to kill your dreams'.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Initialize the TfidfVectorizer 
tfidf = ____

# Construct the TF-IDF matrix
tfidf_matrix = ____

# Generate the cosine similarity matrix
cosine_sim = ____
 
# Generate recommendations 
print(get_recommendations(____, ____, indices))

Edit and Run Code