000			02331nam a22003378i 4500
001			CR9781139565868
003			UkCbUP
005			20200124160216.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			120723s2016||||enk o ||1 0|eng|d
020			_a9781139565868 (ebook)
020			_z9781107036079 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA76.76.E95 _bA395 2016
082	0	0	_a006.3/3 _223
100	1		_aAgarwal, Deepak K., _d1973- _eauthor.
245	1	0	_aStatistical methods for recommender systems / _cDeepak K. Agarwal, Yahoo! Research, Bee Chung-Chen, Yahoo! Research.
264		1	_aCambridge : _bCambridge University Press, _c2016.
300			_a1 online resource (xii, 284 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Feb 2016).
520			_aDesigning algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.
650		0	_aRecommender systems (Information filtering) _xStatistical methods.
650		0	_aExpert systems (Computer science) _xStatistical methods.
700	1		_aChung-Chen, Bee, _eauthor.
776	0	8	_iPrint version: _z9781107036079
856	4	0	_uhttps://doi.org/10.1017/CBO9781139565868
999			_c516260 _d516258