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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 Similarly, record stores have been replaced by big box retailers that have ever--shrinking music departments.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 3642132871
035		▼a (OCoLC)676697658
037		▼a 978-3-642-13286-5 ▼b Springer ▼n http://www.springerlink.com
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050	4	▼a QA76.9.I58 ▼b C45 2010
082	0 4	▼a 004.019 ▼2 22
084		▼a 004.019 ▼2 DDCK
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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과학도서관

No.	소장처	청구기호	등록번호	도서상태	반납예정일	예약	서비스
No. 1	소장처 과학도서관/Sci-Info(2층서고)/	청구기호 004.019 C393m	등록번호 121217294	도서상태 대출가능	반납예정일	예약	서비스 B M

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

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