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  3. Building Recommendation Engines in Python

Exercise

Build the user profiles

You are now able to generate suggestions for similar items based on their labeled features or based on their descriptions. But sometimes finding similar items might not be enough. In the next exercises, you will work through how one could create recommendations based on a user and all the items they liked as opposed to a singular item. You will first generate a profile for a user by aggregating all of the movies they have previously enjoyed.

The tfidf_summary_df you have been working on in the last few exercises has been loaded for you. This contains a row per movie with their titles as the index and a column for each feature containing their respective TF-IDF score.

Instructions 1/2

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  • Create a subset of the tfidf_summary_df that contains only rows corresponding to the supplied list_of_movies_enjoyed list.