Implicit vs. explicit data
As mentioned in the video exercise, feedback used in recommendation engines can be explicit or implicit.
The dataset listening_history_df has been loaded for you.
This dataset contains columns identifying the users, the songs they listen to, along with:
Skipped Track: A Boolean column recording whether the user skipped the song or listened to it to the end.Rating: The score out of 10 the user gave the song.
In this exercise, you will explore the data and from this exploration identify which columns best reflect explicit feedback versus implicit feedback.
This exercise is part of the course
Building Recommendation Engines in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Inspect the listening_history_df DataFrame
print(listening_history_df.____())
# Calculate the number of unique values
print(listening_history_df[['Rating', 'Skipped Track']].____())
# Display a histogram of the values in the Rating column
listening_history_df['Rating'].____()
plt.show()