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
• Implementing ranking algorithms
• Building a filtering model using TensorFlow