If one were to choose an algorithm to showcase data science to the public, a recommendation system would certainly be in the frame. Today, recommendation systems are everywhere. The reason for their popularity is down to their versatility, usefulness, and broad applicability. Whether they are used to recommend products based on user's shopping behavior or to suggest new movies based on viewing preferences, recommenders are now a fact of life. It is even possible that this book was magically suggested based on what marketing companies know about you, such as your social network preferences, your job status, or your browsing history.
In this chapter, we will demonstrate how to recommend music content using raw audio signal. For that purpose, we will cover the following topics:
Using Spark to process audio files stored on HDFS
Learning about Fourier transform for audio signal transformation
Using Cassandra as a caching layer between online and offline...