000			03434nam a22004935i 4500
001			978-3-642-13287-2
003			DE-He213
005			20140220084537.0
007			cr nn 008mamaa
008			100907s2010 gw | s |||| 0|eng d
020			_a9783642132872 _9978-3-642-13287-2
024	7		_a10.1007/978-3-642-13287-2 _2doi
050		4	_aQA75.5-76.95
072		7	_aUNH _2bicssc
072		7	_aUND _2bicssc
072		7	_aCOM030000 _2bisacsh
082	0	4	_a025.04 _223
100	1		_aCelma, Òscar. _eauthor.
245	1	0	_aMusic Recommendation and Discovery _h[electronic resource] : _bThe Long Tail, Long Fail, and Long Play in the Digital Music Space / _cby Òscar Celma.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aXVI, 194p. 60 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aThe Recommendation Problem -- Music Recommendation -- The Long Tail in Recommender Systems -- Evaluation Metrics -- Network-Centric Evaluation -- User-Centric Evaluation -- Applications -- Conclusions and Further Research.
520			_aWith 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 Similarly, record stores have been replaced by big-box retailers that have ever-shrinking music departments.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.
650		0	_aComputer science.
650		0	_aComputational complexity.
650		0	_aInformation storage and retrieval systems.
650		0	_aArtificial intelligence.
650		0	_aMusic.
650	1	4	_aComputer Science.
650	2	4	_aInformation Storage and Retrieval.
650	2	4	_aDiscrete Mathematics in Computer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aMusic.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642132865
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-13287-2
912			_aZDB-2-SCS
999			_c112227 _d112227