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008			160328s2016 gw | s |||| 0|eng d
020			_a9783319296593
040			_aES-MaUEC
050		4	_aQA76.9.I58 _bA343 2016
082	0	4	_a006.312
100	1		_aAggarwal, Charu C. _998701 _0Local
245	1	0	_aRecommender Systems : _bThe Textbook _cby Charu C Aggarwal
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XXI, 498 páginas) _b79 ilustraciones, 18 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aAn Introduction to Recommender Systems -- Neighborhood-Based Collaborative Filtering -- Model-Based Collaborative Filtering -- Content-Based Recommender Systems -- Knowledge-Based Recommender Systems -- Ensemble-Based and Hybrid Recommender Systems -- Evaluating Recommender Systems -- Context-Sensitive Recommender Systems -- Time- and Location-Sensitive Recommender Systems -- Structural Recommendations in Networks -- Social and Trust-Centric Recommender Systems -- Attack-Resistant Recommender Systems -- Advanced Topics in Recommender Systems.
520	3		_aThis book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors. About the Author: Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T.J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has published more than 300 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 15 books, including a textbook on data mining and a comprehensive book on outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). He has also served as program or general chair of many major conferences in data mining. He is a fellow of the SIAM, ACM, and the IEEE, for zcontributions to knowledge discovery and data mining algorithms.y.
710	2		_aSpringerLink (Online service) _0Local _9106996
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988			_aEBOOK, asignarmaterias , EBSPRINGER
650		7	_aInteligencia artificial _0comprobar BNE19900997218 _2embne _9413115
650		7	_aSistemas informáticos _0(OCoLC)1743365 _2embne _0 _9161392
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-29659-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319296593
907			_a.b12948767 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aQA76.9.I58 A343 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11592138 _z06-04-17
999			_c85598 _d85598 _x1