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  3. Building Recommendation Engines in Python

Exercise

Stepping through K-nearest neighbors

You have just seen how K-nearest neighbors can be used to infer how someone might rate an item based on the wisdom of a (similar) crowd. In this exercise, you will step through this process yourself to ensure a good understanding of how it works.

To get you started, as you have generated similarity matrices many times before, that step has been done for you with the user similarity matrix wrapped in a DataFrame loaded as user_similarities.

This has each user as the rows and columns, and where they meet the corresponding similarity score.

In this exercise, you will be working with user_001's similarity scores, find their nearest neighbors, and based on the ratings those neighbors gave a movie, infer what rating user_001 might give it if they saw it.

Instructions

100 XP
  • Find the IDs of User_A's 10 nearest neighbors by extracting the top 10 users in ordered_similarities and storing them as nearest_neighbors.
  • Extract the ratings the users in nearest_neighbors gave from user_ratings_table as neighbor_ratings.
  • Calculate the average rating these users gave to the movie Apollo 13 (1995) to infer what User_A might give it if they had seen it.