Titlebook: Recommender Systems Handbook; Francesco Ricci,Lior Rokach,Bracha Shapira Book 20152nd edition Springer Science+Business Media New York 201

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Explaining Recommendations: Design and Evaluatione are interested in what makes an explanation “good”. The chapter starts by describing how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays w.r.t. explanations. Next, we introduce a number of explanation styles, and how

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Recommender Systems in Industry: A Netflix Case Studyons about how to approach recommendation and many more have been learned since the Grand Prize was awarded in 2009. The evolution of industrial applications of recommender systems has been driven by the availability of different kinds of user data and the level of interest for the area within the re

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Music Recommender Systemskinds of media. We then provide a literature survey of content-based music recommendation, contextual music recommendation, hybrid methods, and sequential music recommendation, followed by overview of evaluation strategies and commonly used data sets. We conclude by pointing to the most important ch

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Social Recommender Systemsered by recommendations and those sites rely on the quality of the recommendations to attract new users and retain existing ones. In this chapter, we introduce the notion of social recommender systems as recommender systems that target the social media domain. After a short introduction, we discuss

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People-to-People Reciprocal Recommendersey must satisfy the preferences and needs of the two parties involved in the recommendation. In contrast, traditional items-to-people recommenders are one-sided and must satisfy only the preference of the person for whom the recommendation is generated. We review the characteristics and present an o

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