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Music recommendation and discovery : the long tail, long fail, and long play in the digital music space

Music recommendation and discovery : the long tail, long fail, and long play in the digital music space (1회 대출)

자료유형
단행본
개인저자
Celma, Oscar.
서명 / 저자사항
Music recommendation and discovery : the long tail, long fail, and long play in the digital music space / Oscar Celma.
발행사항
Berlin ;   Heidelberg :   Springer,   c2010.  
형태사항
xvi, 194 p. : ill. ; 25 cm.
ISBN
9783642132872 3642132871
요약
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 Similarly, record stores have been replaced by big box retailers that have ever--shrinking music departments.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 s.
내용주기
Foreword; Preface; Acknowledgements; Contents; 1 Introduction; 2 The Recommendation Problem; 3 Music Recommendation; 4 The Long Tail in Recommender Systems; 5 Evaluation Metrics; 6 Network-Centric Evaluation; 7 User-Centric Evaluation; 8 Applications; 9 Conclusions and Further Research; Index.
서지주기
Includes bibliographical references and index.
일반주제명
Recommender systems (Information filtering) Music -- Data processing. Human-computer interaction. Music and the Internet.
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020 ▼a 9783642132872
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090 ▼a 004.019 ▼b C393m
100 1 ▼a Celma, Oscar.
245 1 0 ▼a Music recommendation and discovery : ▼b the long tail, long fail, and long play in the digital music space / ▼c Oscar Celma.
260 ▼a Berlin ; ▼a Heidelberg : ▼b Springer, ▼c c2010.
300 ▼a xvi, 194 p. : ▼b ill. ; ▼c 25 cm.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Foreword; Preface; Acknowledgements; Contents; 1 Introduction; 2 The Recommendation Problem; 3 Music Recommendation; 4 The Long Tail in Recommender Systems; 5 Evaluation Metrics; 6 Network-Centric Evaluation; 7 User-Centric Evaluation; 8 Applications; 9 Conclusions and Further Research; Index.
520 ▼a 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 s.
650 0 ▼a Recommender systems (Information filtering)
650 0 ▼a Music ▼x Data processing.
650 0 ▼a Human-computer interaction.
650 0 ▼a Music and the Internet.
945 ▼a KLPA

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