Computing Taste: Algorithms and the Makers of Music Recommendation

Streaming music services are the norm today, but people don’t often think about how they work or how they recommend the next song. Seaver (anthropology, Tufts Univ.; coeditor, Towards an Anthropology of Data) peeks behind the musical curtain in this book about the humans behind the algorithms. Machine learning is not new, but the ways in which algorithms become more exact and observant are evolving as humans learn new programming. An example from the book notes that algorithms do not only identify sound patterns but also lyrics to differentiate music, such as rock music from Christian rock music. The author also humanizes his writing by using the first-person perspective and inserting anecdotes throughout the narrative to break up all of the tech jargon. Music lovers and those who like books about artificial intelligence will enjoy Seaver’s deep dive into the culture, data, and science of music recommendation systems.