Making your first movie recommendations
Now that you have found the most commonly paired movies, you can make your first recommendations!
While you are not taking in any information about the person watching, and do not even know any details about the movie, valuable recommendations can still be made by examining what groups of movies are watched by the same people.
In this exercise, you will examine the movies often watched by the same people that watched Thor
, and then use this data to give a recommendation to someone who just watched the movie.
The DataFrame you generated in the last lesson, combination_counts_df
, that contains counts of how often movies are watched together has been loaded for you.
This exercise is part of the course
Building Recommendation Engines in Python
Exercise instructions
- Order the
combination_counts_df
object from largest to smallest by thesize
column. - Find the newly ordered movie frequencies for the movie
Thor
by subsetting the orderedcombination_counts_df
object wheremovie_a
isThor
assign them tothor_df
and plot the results.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
import matplotlib.pyplot as plt
# Sort the counts from highest to lowest
combination_counts_df.____('size', ascending=____, inplace=____)
# Find the movies most frequently watched by people who watched Thor
thor_df = ____[____['movie_a'] ____ 'Thor']
# Plot the results
thor_df.plot.bar(x="movie_b")
plt.show()