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Music Recommendation and Discovery

The Long Tail, Long Fail, and Long Play in the Digital Music Space

By Òscar Celma

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As more and more of us use automated music recommendation, this book describes how these systems work, explores some of their limitations, offers techniques for evaluating their effectiveness, and uses real-life examples to show how to build them effectively.

Full Description

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  • ISBN13: 978-3-6421-3286-5
  • 212 Pages
  • User Level: Science
  • Publication Date: September 2, 2010
  • Available eBook Formats: PDF
Full Description
While the amount of new music has grown, some of the traditional ways of finding music have diminished.  Thirty years ago, the local radio DJ was a music tastemaker, finding new and interesting music for the local radio audience. Now radio shows are programmed by large corporations that create playlists drawn from a limited pool of tracks. Similarly, record stores have been replaced by big box retailers that have ever--shrinking music departments. In the past, you could always ask the owner of the record store for music recommendations.  You would learn what was new, what was good and what was selling. Now, however, you can no longer expect that the teenager behind the cash register will be an expert in new music, or even be someone who listens to music at all. As we rely more and more on automatic music recommendation it is important for us to understand what makes a good music recommender and how a recommender can affect the world of music. With this knowledge  we can build systems that offer novel, relevant and interesting music recommendations drawn from the entire world of available music. Aimed at final-year-undergraduate and graduate students working on recommender systems or music information retrieval, this book presents the state of the art of all the different techniques used to recommend items, focusing on the music domain as the underlying application.
Table of Contents

Table of Contents

  1. 1 Introduction.
  2. 2 The recommendation problem.
  3. 3 Music recommendation.
  4. 4 The Long Tail in recommender systems.
  5. 5 Evaluation metrics.
  6. 6 Network–centric evaluation.
  7. 7 User–centric evaluation.
  8. 8 Applications.
  9. 9 Conclusions and Further Research.
  10. Index.

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