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Music Recommendation and Discovery

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

  • Òscar Celma

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Òscar Celma
    Pages 1-13
  3. Òscar Celma
    Pages 15-41
  4. Òscar Celma
    Pages 43-85
  5. Òscar Celma
    Pages 87-107
  6. Òscar Celma
    Pages 109-128
  7. Òscar Celma
    Pages 129-156
  8. Òscar Celma
    Pages 157-167
  9. Òscar Celma
    Pages 169-184
  10. Òscar Celma
    Pages 185-191
  11. Back Matter
    Pages 193-194

About this book

Introduction

With so much more music available these days, traditional ways of finding music have diminished. Today radio shows are often 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 Similarly, record stores have been replaced by big-box retailers that have ever-shrinking music departments. Instead of relying on DJs, record-store clerks or their friends for music recommendations, listeners are turning to machines to guide them to new music.

In this book, Òscar Celma guides us through the world of automatic music recommendation. He describes how music recommenders work, explores some of the limitations seen in current recommenders, offers techniques for evaluating the effectiveness of music recommendations and demonstrates how to build effective recommenders by offering two real-world recommender examples. He emphasizes the user's perceived quality, rather than the system's predictive accuracy when providing recommendations, thus allowing users to discover new music by exploiting the long tail of popularity and promoting novel and relevant material ("non-obvious recommendations"). In order to reach out into the long tail, he needs to weave techniques from complex network analysis and music information retrieval.

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.

Keywords

Filtering Algorithms Music Information Retrieval Recommender Systems information retrieval knowledge music retrieval recommender system

Authors and affiliations

  • Òscar Celma
    • 1
  1. 1.BMATBarcelonaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-13287-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-13286-5
  • Online ISBN 978-3-642-13287-2
  • Buy this book on publisher's site