000			02168cam a22002895i 4500
999			_c9060 _d9060
001			21662557
005			20210310165622.0
008			151117s2015 xxu|||| o |||| 0|eng
020			_a9781489976376
040			_aDLC _cDLC
082	0	4	_a025.04 _bBUC
245	0	0	_aRecommender Systems Handbook / _cedited by Francesco Ricci, Lior Rokach, Bracha Shapira.
250			_a2nd ed. 2015.
260			_aBoston, Mass. : _bHarvard Business Review Press, _c 2019
300			_a1 online resource (XVII, 1003 pages 144 illustrations, 78 illustrations in color.) _c cm.
520			_aThis second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems' major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
650		0	_aInformation storage and retrieval.
650		0	_aArtificial intelligence.
650	1	4	_aInformation Storage and Retrieval.
650	2	4	_aArtificial Intelligence.
653			_aTrung tâm thông tin
653			_aLưu trữ và truy xuất thông tin
700	1		_aRicci, Francesco.
700	1		_aRokach, Lior.
700	1		_aShapira, Bracha.
942			_2ddc _cBK