Titlebook: Recommender Systems Handbook; Francesco Ricci,Lior Rokach,Paul B. Kantor Book 20111st edition Springer-Science+Business Media, LLC 2011 Co

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Pearl Pu,Boi Faltings,Li Chen,Jiyong Zhang,Paolo Viappianiare naturally, unconsciously, necessarily ontological, then science would seem to need this ontology ., not in the process of being worked out, but already completely worked out; in other words, it must have made a choice between the possible metaphysical systems.

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ractitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a 978-0-387-85820-3

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Xavier Amatriain,Alejandro Jaimes*,Nuria Oliver,Josep M. Pujol

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Alexander Felfernig,Gerhard Friedrich,Dietmar Jannach,Markus Zanker

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Content-based Recommender Systems: State of the Art and Trendscond part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also prese

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Context-Aware Recommender Systemsduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and pr

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How to Get the Recommender Out of the Lab?, this chapter intends to be a guide on the design, implementation and evaluation of personalised systems. It presents the different aspects that must be studied before the design is even started, and how to avoid pitfalls, in a hands-on approach. The chapter presents the main factors to take into a

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Book 20111st editioninterfaces, decision support systems, marketing, and consumer behavior. Theoreticiansand practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a