#MusicTech
#MusicTech
Experimenting with Data Science and Recommender Systems
Alexandre Passant

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About the Book

Music services (Spotify, iTunes radio, Pandora, etc.) need to stand out; yet, this is getting harder since most catalogs overlap, besides a few exclusives. And, as online music becomes be a commodity, they need to find incentives for users to use them versus competitors.

One way to do so is to invest more time -- both on product and R&D -- on the technology front, especially on personalisation and discovery, in order to be ahead of the pack and own the space. This is an obvious strategy, and a win-win-win for all parties involved: 

  • For consumers, delighted when they discover new artists they will love -- based on their past listening habits or the ones of their friends --; and satisfied as they figure out that streaming services really understand what they like;
  • For artists, escaping the long-tail and hence generating more streams, and a little revenue, but most importantly: having the opportunity to convert casual listeners into super-fans who follow them on tour, buy merch and exclusive records, and more;
  • For streaming services, keeping existing users active with more listening hours and growing their audiences; consequently gathering more data and analytics (plays, thumbs-up, social interactions, etc.), which can be re-invested into product features.

This e-book, #MusicTech, is an evolving summary of various hacks and experiments on data-science, recommender systems - with a focus on online streaming, and more globally analytics and music discovery.

Using tools such as Semantic Web technologies, Big Data infrastructures, Machine Learning, Collaborative Filtering and more, you will learn different techniques to make sense of music streaming data, featuring use-cases which aim to be fun and entertaining, and cover well-known platforms, such as Twitter, Spotify, or YouTube. 

About the Author