Recommender Systems Handbook
Francesco Ricci, Lior Rokach, Bracha Shapira
Springer, Nov 17, 2015 - Computers - 1003 pages
This 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.
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accuracy ACM Conference aggregation functions algorithms application approach Artificial Intelligence behavior chapter choice chooser Choquet integral classifier clustering collaborative filtering Conference on Recommender considered content-based context context-aware recommender contextual information criteria cross-domain Data Mining dataset decision defined diversity domain effect evaluation example explanations explicit Facebook group recommendation IEEE improve Information Retrieval input interaction interface International Conference knowledge Konstan Machine Learning measure methods metrics mobile movie multi-criteria rating Music Information Retrieval music recommendation Netflix Netflix Prize novelty offline optimization parameters personality playlist prediction preference elicitation problem Proc Proceedings query ranking recom recommendation algorithms recommended items recommender systems RecSys relevant Ricci Riedl Sect selected semantic SIGIR similarity social media social networks specific Springer Springer Science+Business Media strategy tags target techniques Technology types User Modeling user preferences user profiles user’s users and items values vector weights York