Identifying latent features
Print original_df
and user_matrix
using the console. The user_matrix
is one of the factors of the original_df
.
Based on the values in the first column of the user_matrix
, what do you think the latent feature may be summarizing?
Note the first row of user_matrix
corresponds to User 1
, the second row to User_2
, and so on.
Remember that latent features tend to represent underlying trends in the data and give items with these underlying trends similar scores.
This exercise is part of the course
Building Recommendation Engines in Python
Hands-on interactive exercise
Turn theory into action with one of our interactive exercises
