000 04188nam a22004935i 4500
001 978-1-4899-7637-6
003 DE-He213
005 20200421111205.0
007 cr nn 008mamaa
008 151117s2015 xxu| s |||| 0|eng d
020 _a9781489976376
_9978-1-4899-7637-6
024 7 _a10.1007/978-1-4899-7637-6
_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
245 1 0 _aRecommender Systems Handbook
_h[electronic resource] /
_cedited by Francesco Ricci, Lior Rokach, Bracha Shapira.
250 _a2nd ed. 2015.
264 1 _aBoston, MA :
_bSpringer US :
_bImprint: Springer,
_c2015.
300 _aXVII, 1003 p. 144 illus., 78 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aRecommender Systems: Introduction and Challenges -- A Comprehensive Survey of Neighborhood-based Recommendation Methods -- Advances in Collaborative Filtering -- Semantics-aware Content-based Recommender Systems -- Constraint-based Recommender Systems -- Context-Aware Recommender Systems -- Data Mining Methods for Recommender Systems -- Evaluating Recommender Systems -- Evaluating Recommender Systems with User Experiments -- Explaining Recommendations: Design and Evaluation -- Recommender Systems in Industry: A Netflix Case Study -- Panorama of Recommender Systems to Support Learning -- Music Recommender Systems -- The Anatomy of Mobile Location-Based Recommender Systems -- Social Recommender Systems -- People-to-People Reciprocal Recommenders -- Collaboration, Reputation and Recommender Systems in Social Web Search -- Human Decision Making and Recommender Systems -- Privacy Aspects of Recommender Systems -- Source Factors in Recommender System Credibility Evaluation -- Personality and Recommender Systems -- Group Recommender Systems: Aggregation, Satisfaction and Group Attributes -- Aggregation Functions for Recommender Systems -- Active Learning in Recommender Systems -- Multi-Criteria Recommender Systems -- Novelty and Diversity in Recommender Systems -- Cross-domain Recommender Systems -- Robust Collaborative Recommendation.
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 _aComputer science.
650 0 _aInformation storage and retrieval.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aRicci, Francesco.
_eeditor.
700 1 _aRokach, Lior.
_eeditor.
700 1 _aShapira, Bracha.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781489976369
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4899-7637-6
912 _aZDB-2-SCS
942 _cEBK
999 _c54108
_d54108