Python Machine Learning Cookbook

Chapter 5. Building Recommendation Engines

In this chapter, we will cover the following recipes:

• Building function compositions for data processing
• Building machine learning pipelines
• Finding the nearest neighbors
• Constructing a k-nearest neighbors classifier
• Constructing a k-nearest neighbors regressor
• Computing the Euclidean distance score
• Computing the Pearson correlation score
• Finding similar users in the dataset
• Generating movie recommendations

Introduction

A recommendation engine is a model that can predict what a user may be interested in. When we apply this to the context of movies, this becomes a movie-recommendation engine. We filter items in our database by predicting how the current user might rate them. This helps us in connecting the users ...