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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Usability Guidelines for Product Recommenders Based on Example Critiquing Researchommerce environments. In this chapter, we survey important usability research work relative to example critiquing and summarize the major results by deriving a set of usability guidelines. Our survey is focused on three key interaction activities between the user and the system: the initial preferen

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Data Mining Methods for Recommender Systemsnd Support Vector Machines. We describe the .-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

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A Comprehensive Survey of Neighborhood-based Recommendation Methodsd gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and a few solutions to overcome these problems are presented.

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Designing and Evaluating Explanations for Recommender Systemsteraction with the recommender system plays w.r.t. explanations. Finally, we describe a number of explanation styles, and how they may be related to the underlying algorithms. Examples of explanations in existing systems are mentioned throughout.

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A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment are extensively analyzed by means of off-line and on-line tests, showing the effectiveness of the recommender systems: up to 30% of the recommendations are followed by a purchase, with an estimated lift factor (increase in sales) of 15%.

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Matching Recommendation Technologies and Domainsknowledge come from? Different recommendation domains (books vs condominiums, for example) provide different opportunities for the gathering and application of knowledge. These considerations give rise to a mapping between domain characteristics and recommendation technologies.

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