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Music Recommendation and Discovery

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

  • Book
  • © 2010

Overview

Authors:
  1. Òscar Celma

    1. BMAT, Barcelona, Spain

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  • Starts with a formalization of the general recommendation problem
  • Presents the pros and cons of most-used recommendation approaches, with a focus on the music domain
  • Combines elements from recommender systems, complex network analysis, music information retrieval, and personalization
  • Emphasizes "user's perceived quality" versus "system's predictive accuracy"
  • Includes supplementary material: sn.pub/extras

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Introduction

    • Òscar Celma
    Pages 1-13

  3. The Recommendation Problem

    • Òscar Celma
    Pages 15-41

  4. Music Recommendation

    • Òscar Celma
    Pages 43-85

  5. The Long Tail in Recommender Systems

    • Òscar Celma
    Pages 87-107

  6. Evaluation Metrics

    • Òscar Celma
    Pages 109-128

  7. Network-Centric Evaluation

    • Òscar Celma
    Pages 129-156

  8. User-Centric Evaluation

    • Òscar Celma
    Pages 157-167

  9. Applications

    • Òscar Celma
    Pages 169-184

  10. Conclusions and Further Research

    • Òscar Celma
    Pages 185-191

  11. Back Matter

    Pages 193-194
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Keywords

About this book

In the last 15 years we have seen a major transformation in the world of music. - sicians use inexpensive personal computers instead of expensive recording studios to record, mix and engineer music. Musicians use the Internet to distribute their - sic for free instead of spending large amounts of money creating CDs, hiring trucks and shipping them to hundreds of record stores. As the cost to create and distribute recorded music has dropped, the amount of available music has grown dramatically. Twenty years ago a typical record store would have music by less than ten thousand artists, while today online music stores have music catalogs by nearly a million artists. While the amount of new music has grown, some of the traditional ways of ?nding music have diminished. Thirty years ago, the local radio DJ was a music tastemaker, ?nding new and interesting music for the local radio audience. Now - dio 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 reta- ers 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.

Authors and Affiliations

  • BMAT, Barcelona, Spain

    Òscar Celma

Bibliographic Information

  • Book Title: Music Recommendation and Discovery

  • Book Subtitle: The Long Tail, Long Fail, and Long Play in the Digital Music Space

  • Authors: Òscar Celma

  • DOI: https://doi.org/10.1007/978-3-642-13287-2

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag GmbH Berlin Heidelberg 2010

  • Hardcover ISBN: 978-3-642-13286-5Published: 05 September 2010

  • Softcover ISBN: 978-3-642-43953-7Published: 15 October 2014

  • eBook ISBN: 978-3-642-13287-2Published: 02 September 2010

  • Edition Number: 1

  • Number of Pages: XVI, 194

  • Topics: Information Storage and Retrieval, Discrete Mathematics in Computer Science, Artificial Intelligence, Music

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