000			03684cam a2200553Ii 4500
001			9780367631888
003			FlBoTFG
005			20220711212248.0
006			m o d
007			cr cnu---unuuu
008			210520s2021 flua ob 001 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781000387278 _q(electronic bk.)
020			_a1000387275 _q(electronic bk.)
020			_a9780367631888 _q(electronic bk.)
020			_a0367631881 _q(electronic bk.)
020			_a9781000387377 _q(electronic bk. : EPUB)
020			_a1000387372 _q(electronic bk. : EPUB)
020			_z0367631857
020			_z9780367631857
035			_a(OCoLC)1251926845
035			_a(OCoLC-P)1251926845
050		4	_aZA3084
072		7	_aCOM _x004000 _2bisacsh
072		7	_aCOM _x051300 _2bisacsh
072		7	_aBUS _x016000 _2bisacsh
072		7	_aUYQ _2bicssc
082	0	4	_a005.5/6 _223
245	0	0	_aRecommender systems : _balgorithms and applications / _cedited by P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nandan Mohanty.
250			_aFirst edition.
264		1	_aBoca Raton : _bCRC Press, _c2021.
300			_a1 online resource : _billustrations (some color)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
520			_aRecommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms, including Machine learning algorithms Community detection algorithms Filtering algorithms Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include A latent-factor technique for model-based filtering systems Collaborative filtering approaches Content-based approaches Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aRecommender systems (Information filtering) _99125
650		7	_aCOMPUTERS / Artificial Intelligence _2bisacsh _912740
650		7	_aCOMPUTERS / Programming / Algorithms _2bisacsh _913132
650		7	_aBUSINESS & ECONOMICS / Consumer Behavior _2bisacsh _914827
700	1		_aKumar, P. Pavan, _eeditor. _914828
700	1		_aVairachilai, S., _eeditor. _914829
700	1		_aPotluri, Sirisha, _eeditor. _914830
700	1		_aMohanty, Sachi Nandan, _eeditor. _914831
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780367631888
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942			_cEBK
999			_c70806 _d70806