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

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Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space Cover

 

Synopses & Reviews

Publisher Comments:

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 Similarly, record stores have been replaced by big-box retailers that have ever-shrinking music departments.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,‛scar 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.

Synopsis:

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.

Synopsis:

While the amount of new music has grown, some of the traditional ways of finding music have diminished.

Table of 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.

Product Details

ISBN:
9783642132865
Author:
Celma, Oscar
Publisher:
Springer
Subject:
Internet-Information
Subject:
Information Storage & Retrieval
Subject:
Filtering Algorithms
Subject:
Information retrieval
Subject:
music retrieval
Subject:
recommender systems
Subject:
Information storage and retrieval.
Subject:
Discrete Mathematics in Computer Science
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Music
Copyright:
Edition Description:
2010
Publication Date:
20100907
Binding:
HARDCOVER
Language:
English
Pages:
210
Dimensions:
235 x 155 mm 485 gr

Related Subjects

» Computers and Internet » Artificial Intelligence » General
» Computers and Internet » Computers Reference » General
» Computers and Internet » Internet » Information
» Metaphysics » UFOs
» Science and Mathematics » Astronomy » Extraterrestrial
» Science and Mathematics » Mathematics » Advanced
» Science and Mathematics » Physics » Astrophysics

Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space New Hardcover
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Product details 210 pages Not Avail - English 9783642132865 Reviews:
"Synopsis" by , 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.
"Synopsis" by , While the amount of new music has grown, some of the traditional ways of finding music have diminished.
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